Patent Publication Number: US-2018047035-A1

Title: Analysis device, analysis method, and computer-readable recording medium

Description:
TECHNICAL FIELD 
     The present invention relates to an analysis device, an analysis method, and a computer-readable recording medium. 
     BACKGROUND ART 
     In product planning and sales, prediction relating to an occurrence of brand switch (that a customer who has previously purchased Product A purchases another Product B similar to Product A), and exploration relating to an area where there are fewer similar rival products for differentiation of a new product may be required. When such prediction and the like are performed, a relation of similarity between products is often obtained by quantitatively calculating similarity between combinations of products which belong to a category as an analysis target, by using a certain method. 
     PTL 1 describes a technique relating to a POS (Point Of Sales) data analysis device and others. The POS data analysis device others described in PTL 1 calculates similarity (a degree of concurrent sales) between products by using concurrent sales characteristic information. In the technique described in PTL 1, examples of the concurrent sales characteristic information include Simpson&#39;s coefficient, Jaccard coefficient, and the like. In addition, in the technique described in PTL 1, when vector information is used, Cosine similarity, Pearson&#39;s correlation coefficient, and the like are used as the concurrent sales characteristic information. 
     In addition, NPL 1 describes a method of visualizing POS data. In the method described in NPL 1, a sales status/a concurrent sales status in POS data is visualized by excluding, by using Fisher&#39;s exact probability test, concurrent sales information which seems to have no clear intention. 
     CITATION LIST 
     Patent Literature 
     
         
         [PTL 1] Japanese Unexamined Patent Application Publication No. 2011-258023 
       
    
     Non Patent Literature 
     
         
         [NPL 1] Takahide HOSHIDE, Ko FUJIMURA, Tatsushi MATSUBAYASHI, “Visualizing Point-of-sales Data using Virtual Topographic Map”, IEICE technical research report, LOIS2009-98, March, 2010 
       
    
     SUMMARY OF INVENTION 
     Technical Problem 
     In the method described in PTL 1 or NPL 1, similarity between products is obtained based on a degree of concurrent sales (frequency with which a plurality of products are concurrently purchased by a certain customer) of the respective products. However, there is a case where it is difficult to derive similarity between products based on a degree of concurrent sales of the products, depending on products for which similarity is to be obtained. 
     The present invention has been made in order to solve the problem described above, and a main object of the present invention is to provide an analysis device and others which are capable of more accurately deriving similarity between products. 
     Solution to Problem 
     An analysis device according to an aspect of the present invention includes: first concurrent sales quantity derivation means for deriving a first concurrent sales quantity that is a sales volume of a product purchased concurrently with a first product, first concurrent sales vector derivation means for deriving, based on the first concurrent sales quantity, a first concurrent sales vector representing a relation among a plurality of products purchased concurrently with the first product, second concurrent sales quantity derivation means for deriving a second concurrent sales quantity that is a sales volume of a product purchased concurrently with a second product, second concurrent sales vector derivation means for deriving, based on the second concurrent sales quantity, a second concurrent sales vector representing a relation among a plurality of products purchased concurrently with the second product, and similarity derivation means for deriving, based on the first concurrent sales vector and the second concurrent sales vector, similarity between the first product and the second product. 
     In addition, an analysis method according to an aspect of the present invention deriving a first concurrent sales quantity that is a sales volume of a product purchased concurrently with a first product; 
     deriving, based on the first concurrent sales quantity, a first concurrent sales vector representing a relation among a plurality of products purchased concurrently with the first product; 
     deriving a second concurrent sales quantity that is a sales volume of a product purchased concurrently with a second product; 
     deriving, based on the second concurrent sales quantity, a second concurrent sales vector representing a relation among a plurality of products purchased concurrently with the second product; and 
     deriving, based on the first concurrent sales vector and the second concurrent sales vector, similarity between the first product and the second product. 
     In addition, a computer-readable recording medium according to an aspect of the present invention non-transitorily stores a program that causes a computer to execute processing of deriving a first concurrent sales quantity that is a sales volume of a product purchased concurrently with a first product, deriving, based on the first concurrent sales quantity, a first concurrent sales vector representing a relation among a plurality of products purchased concurrently with the first product, deriving a second concurrent sales quantity that is a sales volume of a product purchased concurrently with a second product, deriving, based on the second concurrent sales quantity, a second concurrent sales vector representing a relation among a plurality of products purchased concurrently with the second product, and deriving, based on the first concurrent sales vector and the second concurrent sales vector, similarity between the first product and the second product. 
     Advantageous Effects of Invention 
     According to the present invention, it is possible to provide an analysis device and others which are capable of more accurately deriving similarity between products. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram illustrating a configuration of an analysis device according to a first example embodiment of the present invention; 
         FIG. 2  is a diagram illustrating an example of sales data used in the analysis device according to the first example embodiment of the present invention; 
         FIG. 3  is a diagram illustrating an example of a first concurrent sales vector or a second concurrent sales vector used in the analysis device according to the first example embodiment of the present invention; 
         FIG. 4  is a flowchart illustrating an operation of the analysis device according to the first example embodiment of the present invention; 
         FIG. 5  is a diagram illustrating a configuration of a modification example of the analysis device according to the first example embodiment of the present invention; 
         FIG. 6  is a diagram illustrating a configuration of an analysis device according to a second example embodiment of the present invention; 
         FIG. 7  is a diagram illustrating an example of sales data used in the analysis device according to the second example embodiment of the present invention; 
         FIG. 8  is a flowchart illustrating an operation of the analysis device according to the second example embodiment of the present invention; 
         FIG. 9  is a diagram illustrating a configuration of a modification example of the analysis device according to the second example embodiment of the present invention; 
         FIG. 10  is a diagram illustrating a configuration of an analysis system according to a third example embodiment of the present invention; and 
         FIG. 11  is a diagram illustrating an example of an information processing device which implements an analysis device and the like according to each of the example embodiments of the present invention. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Example embodiments of the present invention will be described with reference to the accompanying drawings. Note that, in each of the example embodiments of the present invention, components in each device indicate blocks on a function basis. Components in each device can be implemented by any combination of, for example, an information processing device  500  as illustrated in  FIG. 11  and software. As an example, the information processing device  500  includes configurations as follows.
         a CPU (Central Processing Unit)  501     a ROM (Read Only Memory)  502     a RAM (Random Access Memory)  503     a program  504  loaded on the RAM  503     a storage device  505  for storing the program  504     a drive device  507  for reading and writing a recording medium  506     a communication interface  508  connected with a network  509     an input/output interface  510  for inputting and outputting data   a bus  511  connecting the respective components       

     Components in each device according to each of the example embodiments are implemented by the CPU  501  acquiring and executing the program  504  which implements these functions. The program  504  which implements the functions of the components in each device is stored in advance in, for example, the storage device  505  and the RAM  503 , and is read out by the CPU  501  as needed. Note that the program  504  may be supplied to the CPU  501  via the communication network  509 , or the program, which is stored in advance in the recording medium  506 , may be read out and supplied to the CPU  501  by the drive device  507 . 
     There are various modification examples of a method of implementing each device. For example, each device may be implemented by any combination of the information processing device  500  and a program which are respectively different for each component. In addition, a plurality of components included in each device may be implemented by any combination of one information processing device  500  and a program. 
     In addition, part or all of components in each device are implemented by a general-purpose or dedicated circuitry, a processor, and the like, or a combination thereof. These may be configured by a single chip, or may be configured by a plurality of chips connected through a bus. Part or all of components in each device may be implemented by a combination of the above-described circuitry or the like and a program. 
     When part or all of components in each device are implemented by a plurality of information processing devices, circuitries, and the like, the plurality of information processing devices, the circuitries, and the like may be centralizedly arranged, or may be dispersedly arranged. For example, information processing devices, circuitries, and the like may be implemented as a mode, such as a client and server system, and a cloud computing system, in which the information processing devices, the circuitries, and the like are respectively connected via a communication network. 
     First Example Embodiment 
     First, a first example embodiment of the present invention will be described.  FIG. 1  is a diagram illustrating an analysis device according to the first example embodiment of the present invention.  FIG. 2  is a diagram illustrating an example of sales data used in the analysis device according to the first example embodiment of the present invention.  FIG. 3  is a diagram illustrating an example of a first concurrent sales vector or a second concurrent sales vector used in the analysis device according to the first example embodiment of the present invention.  FIG. 4  is a flowchart illustrating an operation of the analysis device according to the first example embodiment of the present invention.  FIG. 5  is a diagram illustrating a configuration of a modification example of the analysis device according to the first example embodiment of the present invention. 
     As illustrated in  FIG. 1 , an analysis device  100  according to the first example embodiment of the present invention includes a first concurrent sales quantity derivation unit  111 , a first concurrent sales vector derivation unit  121 , a second concurrent sales quantity derivation unit  112 , a second concurrent sales vector derivation unit  122 , and a similarity derivation unit  130 . 
     The first concurrent sales quantity derivation unit  111  derives a first concurrent sales quantity which is a sales volume of a product purchased concurrently with a first product. The first concurrent sales vector derivation unit  121  derives, based on the first concurrent sales quantity, a first concurrent sales vector which represents a relation among a plurality of products purchased concurrently with the first product. The second concurrent sales quantity derivation unit  112  derives a second concurrent sales quantity which is a sales volume of a product purchased concurrently with a second product. The second concurrent sales vector derivation unit  122  derives, based on the second concurrent sales quantity, a second concurrent sales vector which represents a relation among a plurality of products purchased concurrently with the second product. The similarity derivation unit  130  derives, based on the first concurrent sales vector and the second concurrent sales vector, similarity between the first product and the second product. 
     In the present example embodiment, a first product and a second product are any kinds of two products as targets for derivation of similarity between these products. Note that a product similar to a certain product is, for example, a product which has a function, a feature, and other features in common with the certain product, and which may be possibly released for sale or the like, as an alternative to the product, to a user of the product or others. Similarity between products is a degree obtained by quantifying the above-described similarity relating to at least two products by using any method. 
     In addition, in the present example embodiment, a sales volume indicates a quantity or a sales amount of a sold product. Hereinafter, in description of each of the example embodiments of the present invention, a sales volume will be described as being a sales quantity. Concurrent sales mean that a plurality of products are concurrently purchased by a certain customer (in other words, a plurality of products are concurrently sold to a certain customer). 
     Note that, in the present example embodiment, a first concurrent sales quantity and a second concurrent sales quantity may be collectively called as “concurrent sales quantity”. In addition, a first concurrent sales vector and a second concurrent sales vector may be collectively called as a “concurrent sales vector”. 
     Subsequently, each components of the analysis device  100  according to the present example embodiment will be described. 
     As described above, the first concurrent sales quantity derivation unit  111  derives a first concurrent sales quantity which is a sales volume of a concurrently purchased product, such as being purchased at the same time with a first product through one transaction. As an example, the first concurrent sales quantity derivation unit  111  derives, for each product, a total sales volume of a product purchased concurrently with a first product as a first concurrent sales quantity, based on sales data representing a purchase (sales) status of a product, etc. including the first product prepared in advance. 
     Sales data are data obtained by collecting information relating to a product, etc. purchased through one transaction with a customer, etc. A product, etc. purchased through one transaction may be plural in information included in sales data. The first concurrent sales quantity derivation unit  111  derives a first concurrent sales quantity, based on information in which a first product is included in purchased products among the above-described sales data. 
       FIG. 2  is a diagram illustrating an example of sales data. The example of sales data illustrated in  FIG. 2  is based on POS data. In the present example embodiment, a serial number as, for example, a basket number is assigned to each piece of information on the above-described one transaction included in sales data. The example of sales data illustrated in  FIG. 2  indicates information relating to transactions with basket number “1000001” and basket number “1000002”. In addition, in  FIG. 2 , a quantity of products purchased through two transactions represented by the above-described two basket numbers is indicated. In addition, Products 1 to 4 are allocated with product numbers “30001” to “30004”, respectively. 
     In this example, when it is assumed that a first product is Product 1 and a second product is Product 4, Product 1, which is the first product, is purchased in both pieces of information relating to two transactions included in the sales data. Accordingly, the first concurrent sales quantity derivation unit  111  derives a first concurrent sales quantity, based on the both pieces of information. In other words, the first concurrent sales quantity derivation unit  111  derives a first concurrent sales quantity as 2, 4, 3, and 1 for Product 1, Product 2, Product 3, and Product 4, respectively. 
     The second concurrent sales quantity derivation unit  112  derives a second concurrent sales quantity which is a sales volume of a product purchased concurrently with a second product. As an example, the second concurrent sales quantity derivation unit  112  may derive a second concurrent sales quantity, based on sales data including a second product prepared in advance, by using the similar method as that of the first concurrent sales quantity derivation unit  111 . 
     In the example of sales data illustrated in  FIG. 2  described above, Product 4, which is the second product, is purchased only through a transaction with basket number “1000001”. Accordingly, the second concurrent sales quantity derivation unit  112  derives a second concurrent sales quantity, based on information relating to the transaction with basket number “1000001”. In other words, the second concurrent sales quantity derivation unit  112  derives a second concurrent sales quantity as 1, 2, 1, and 1 for Product 1, Product 2, Product 3, and Product 4, respectively. 
     The first concurrent sales vector derivation unit  121  derives, based on the first concurrent sales quantity, a first concurrent sales vector which represents a relation among a plurality of products purchased concurrently with the first product. As one example, the first concurrent sales vector derivation unit  121  excludes the first product and the second product, and derives a set representing sales volumes of other products as a first concurrent sales vector. However, the first concurrent sales vector derivation unit  121  may derive a first concurrent sales vector without excluding the first product and the second product. 
     In the example illustrated in  FIG. 2  described above, a first concurrent sales quantity is 2 for Product 2, 4 for Product 3, and 1 for Product 4, respectively, with respect to Product 1 which is the first product. In this case, the first concurrent sales vector derivation unit  121  excludes Product 1 which is the first product and Product 4 which is the second product, and derives a first concurrent sales vector as (4, 3). 
     In addition,  FIG. 3  illustrates an example of a first concurrent sales vector and a second concurrent sales vector with respect to a first product with product number “40001” and a second product with product number “40002”. A table illustrated in  FIG. 3  shows that one hundred products with product number “10001”, two hundred products with product number “10002”, and three hundred products with product number “99999” are purchased (sold) concurrently with the first product. In other words, when products purchased concurrently with the first product are the above-described three products, the first concurrent sales vector derivation unit  121  derives as (100, 200, 300). 
     The second concurrent sales vector derivation unit  122  derives, based on the second concurrent sales quantity, a second concurrent sales vector which represents a relation among a plurality of products purchased concurrently with the second product. The second concurrent sales vector derivation unit  122  may derive second concurrent sales vector by using the similar method as that of the first concurrent sales vector derivation unit  121 . As an example, the second concurrent sales vector derivation unit  122  excludes the first product and the second product, and derives a set representing sales volumes of other products as a second concurrent sales vector. The second concurrent sales vector derivation unit  122  may derive a second concurrent sales vector without excluding the first product and the second product. 
     In addition, the table illustrated in  FIG. 3  shows that ten products with product number “10001”, twenty products with product number “10002”, and forty products with product number “99999” are purchased (sold) concurrently with the second product. In other words, when products purchased concurrently with the second product are the above-described three products, the second concurrent sales vector derivation unit  122  derives a first concurrent sales vector as (10, 20, 40). 
     Note that each of the first concurrent sales vector derivation unit  121  and the second concurrent sales vector derivation unit  122  may derive various aspects of concurrent sales vectors. For example, each of the first concurrent sales vector derivation unit  121  and the second concurrent sales vector derivation unit  122  may obtain a concurrent sales vector regarding all products sold concurrently with the first product or the second product. In addition, each of the first concurrent sales vector derivation unit  121  and the second concurrent sales vector derivation unit  122  may obtain a concurrent sales vector based on a sales volume relating to a product, whose sales quantity satisfies a predetermined condition, among products sold concurrently with the first product or the second product. In this case, each of the first concurrent sales vector derivation unit  121  and the second concurrent sales vector derivation unit  122  obtains, for example, a concurrent sales vector regarding a product, whose sales quantity is not less than a predetermined quantity, among products sold concurrently with the first product or the second product. 
     In addition, each of the first concurrent sales vector derivation unit  121  and the second concurrent sales vector derivation unit  122  may obtain a concurrent sales vector regarding one or more products that are determined as representative products by any criterion, from among respective products classified in each category. Besides the above, each of the first concurrent sales vector derivation unit  121  and the second concurrent sales vector derivation unit  122  may obtain a concurrent sales vector regarding a product, that is determined as having influence on similarity between the first product and the second product by any method, among products sold concurrently with the first product or the second product. 
     The similarity derivation unit  130  derives, based on the first concurrent sales vector and the second concurrent sales vector, similarity between the first product and the second product. As an example, the similarity derivation unit  130  derives similarity in such a way as to result in a higher similarity when the first concurrent sales vector and the second concurrent sales vector are close to each other. In other words, the similarity derivation unit  130  derives similarity, based on a concept that the first product is similar to the second product with high possibility when a group of products purchased together with the first product are similar to a group of products purchased together with the second product. In the similarity derivation unit  130 , Cosine similarity, Pearson&#39;s correlation coefficient, or the like is used as an index of similarity. In addition, in the similarity derivation unit  130 , Spearman&#39;s rank correlation coefficient, Kendall&#39;s rank correlation coefficient, or the like may be used as an index of similarity. 
     In the example of a concurrent sales vector illustrated in  FIG. 3  described above, the similarity derivation unit  130  calculates similarity between the first concurrent sales vector and the second concurrent sales vector as 0.982, by using Pearson&#39;s correlation coefficient. 
     Subsequently, an example of an operation of the analysis device  100  according to the first example embodiment of the present invention will be described by using a flowchart illustrated in  FIG. 4 . 
     First, the first concurrent sales quantity derivation unit  111  derives a first concurrent sales quantity (Step S 101 ). Next, the first concurrent sales vector derivation unit  121  derives a first concurrent sales vector, based on the first concurrent sales quantity derived at Step S 101  (Step S 102 ). 
     Along with the operations of Steps S 101  and S 102 , the second concurrent sales quantity derivation unit  112  derives a second concurrent sales quantity (Step S 103 ). In addition, subsequent to the process of Step S 103 , the first concurrent sales vector derivation unit  121  derives a second concurrent sales vector (Step S 104 ). As an example, the processes of Steps S 103  and S 104  are respectively performed in the similar way as the processes of Steps S 101  and S 102 . 
     Note that the flowchart illustrated in  FIG. 4  indicates that the operations of Steps S 101  and S 102  and the operations of Steps S 103  and S 104  are concurrently performed. However, these operations may be performed in different order from that of the example illustrated in  FIG. 4 . For example, the processes from Steps S 101  to S 104  may be executed sequentially in this order. 
     Finally, the similarity derivation unit  130  derives similarity between a first product and a second product (Step S 105 ). The similarity between the first product and the second product derived by the similarity derivation unit  130  is output from, for example, any output means including a display device, a communication network, or any other means. In addition, the similarity between the first product and the second product derived by the similarity derivation unit  130  may be stored in any kinds of storage means in such a way that it can referred when needed. 
     As described above, the analysis device  100  according to the first example embodiment of the present invention derives similarity between a first product and a second product, based on concurrent sales vectors relating to a quantity of products sold concurrently with the first product and the second product. 
     As described in PTL 1, in a technique of determining similarity between two products as being high when the two products are purchased at the same time, there is a case where it is difficult to obtain similarity regarding products or others that are less frequently purchased at the same time. In contrast, the analysis device  100  according to the present example embodiment derives similarity so as to result in a higher similarity relating to two products when products purchased at the same time with the two products are similar to the two products. 
     In other words, the analysis device  100  according to the present example embodiment is able to derive similarity relating to two products, regardless of frequency with which the two products are purchased at the same time. Therefore, the analysis device  100  according to the present example embodiment is able to derive similarity between products more accurately. 
     Modification Example of First Example Embodiment 
     There are various modification examples conceivable as the analysis device  100  according to the present example embodiment. As a modification example, the analysis device  100  according to the present example embodiment may include a means for acquiring and managing sales data.  FIG. 5  illustrates a configuration of an analysis device  100 A according to the modification example. 
     The analysis device  100 A illustrated in  FIG. 5  further includes, on the analysis device  100  according to the present example embodiment, a sales data management unit  150 , a sales data storage unit  160 , a first product data selection unit  171 , and a second product data selection unit  172 . 
     The sales data management unit  150  receives sales data from outside. As an example, the sales data management unit  150  acquires, as sales data, POS data obtained from a POS terminal or the like disposed in a store, etc., sales result data collected at a time of product selling through an online shop or other shops, and other data via a not-illustrated communication network, etc. In addition, the sales data management unit  150  stores the acquired sales data in, for example, the sales data storage unit  160 . 
     In addition, the sales data storage unit  160  stores sales data relating to a first product and a second product. 
     The first product data selection unit  171  selects and acquires, from among sales data, information relating to a transaction including a first product. Likewise, the second product data selection unit  172  selects and acquires, from among sales data, sales data relating to a transaction including a second product. 
     Each of the first product data selection unit  171  and the second product data selection unit  172  reads sales data stored in the sales data storage unit  160  and selects the sales data. Each of the first product data selection unit  171  and the second product data selection unit  172  may acquire sales data directly from the sales data management unit  150  and may select desired information. 
     Note that, in this modification example, the first concurrent sales quantity derivation unit  111  derives a first concurrent sales quantity, based on sales data relating to a transaction including a first product which are selected by the first product data selection unit  171 . Likewise, in this modification example, the second concurrent sales quantity derivation unit  112  derives a second concurrent sales quantity, based on sales data relating to a transaction including a second product which are selected by the second product data selection unit  172 . Thus, each of the first concurrent sales quantity derivation unit  111  and the second concurrent sales quantity derivation unit  112  is able to derive a first concurrent sales quantity or a second concurrent sales quantity fast. 
     The first concurrent sales quantity derivation unit  111  and the second concurrent sales quantity derivation unit  112  may be configured as a single concurrent sales quantity derivation unit. In this case, the concurrent sales quantity derivation unit derives a first concurrent sales quantity or a second concurrent sales quantity as appropriate in accordance with input data. Likewise, the first concurrent sales vector derivation unit  121  and the second concurrent sales vector derivation unit  122  may be configured as a single concurrent sales vector derivation unit. 
     In addition, the components in the analysis device  100  according to the present example embodiment and the modification example thereof may be implemented as respectively single devices. In this case, as an example, a device which receives a first concurrent sales vector and a second concurrent sales vector obtained by another physically or logically independent device, etc., and which derives similarity between a first product and a second product is implemented as a device for implementing the similarity derivation unit  130 . In addition, in this case, devices for implementing the components in the analysis device  100  and the modification example thereof are respectively connected with one another via, for example, a wired or wireless communication network or other means. 
     Note that, in the present example embodiment, each of the analysis device  100  and the modification example thereof has been described as deriving similarity between products. However, each of the analysis device  100  and the modification example thereof is also possible to derive similarity between other targets. For example, each of the analysis device  100  and the modification example thereof is able to derive similarity between movies or travel destinations. In this case, similarity or other features between movies or travel destinations is derived based on data on favorite movies, travel destinations, etc. which are aggregated for each movie viewer or traveler, instead of data such as the above-described sales data. 
     Second Example Embodiment 
     Next, a second example embodiment according to the present invention will be described.  FIG. 6  is a diagram illustrating a configuration of an analysis device according to the second example embodiment of the present invention.  FIG. 7  is a diagram illustrating an example of sales data used in the analysis device according to the second example embodiment of the present invention.  FIG. 8  is a flowchart illustrating an operation of the analysis device according to the second example embodiment of the present invention.  FIG. 9  is a diagram illustrating a configuration of a modification example of the analysis device according to the second example embodiment of the present invention. 
     As illustrated in  FIG. 6 , an analysis device  200  according to the second example embodiment of the present invention includes a second product data exclusion unit  241 , a first product data exclusion unit  242 , a first concurrent sales quantity derivation unit  111 , a first concurrent sales vector derivation unit  121 , a second concurrent sales quantity derivation unit  112 , a second concurrent sales vector derivation unit  122 , and a similarity derivation unit  130 . 
     The second product data exclusion unit  241  derives first sales data which include information on purchased products (or information on sold products) when the first product and at least one product different from the second product are concurrently purchased by a customer. The first product data exclusion unit  242  derives second sales data which include information on purchased products (or information on sold products) when the second product and at least one product different from the first product are concurrently purchased by a customer. In addition, in the present example embodiment, the first concurrent sales quantity derivation unit  111  derives the first concurrent sales quantity, based on the first sales data derived by the second product data exclusion unit  241 . The second concurrent sales quantity derivation unit  112  derives the second concurrent sales quantity, based on the second sales data derived by the first product data exclusion unit  242 . Configurations other than the above of the analysis device  200  according to the present example embodiment can be made similarly as those of the analysis device  100  according to the first example embodiment of the present invention. 
     The second product data exclusion unit  241  derives first sales data, as described above. The first sales data can be considered as being sales data from which information relating to a transaction such that a first product and a second product are concurrently purchased (or sold) is excluded, among sales data relating to the first product. As an example, the second product data exclusion unit  241  derives, from among the above-described sales data, information in which a first product is included in purchased products but a second product is not included therein, as first sales data. 
     In addition, the first product data exclusion unit  242  derives second sales data similarly as the second product data exclusion unit  241 . The second sales data can be considered as being sales data from which information relating to a transaction s a second product and a first product are concurrently purchased (or sold) is excluded, among sales data relating to the second product. As an example, the first product data exclusion unit  242  derives, from among the above-described sales data, information in which a second product is included in purchased products but a first product is not included therein, as second sales data. 
     In general, products that are frequently purchased concurrently at a time may be dissimilar to each other (in other words, similarity is low). Note that a product that is dissimilar to a certain product is a product which includes a different function and a different feature from the certain product, and which is regarded as having a low similarity. 
     As an example, sweet bread (for example, sweet buns) and non-sweet bread (for example, stuffed bread) are considered as being dissimilar products, because of different tastes. Sweet bread and non-sweet bread may be purchased at the same time. This can be considered that sweet bread and non-sweet bread are purchased at the same time, since the both have dissimilar tastes and can be eaten continuously without getting tired. In other words, products that are dissimilar to each other may be concurrently purchased because of the dissimilarity. However, it is conceivable that, when information on a case where products that are dissimilar to each other are concurrently purchased is used for deriving similarity between two products, the similarity may not be obtained correctly. 
     In view of the above, the second product data exclusion unit  241  derives first sales data. The first sales data are sales data from which information relating to a transaction when a first product and a second product, which are targets for deriving similarity, are purchased at the same time is excluded. In addition, the first product data exclusion unit  242  derives second sales data which are similar kind of sales data. By using the first sales data or the second sales data, the analysis device  200  according to the present example embodiment is able to derive similarity between a first product and a second product in consideration that there is a case where products that are dissimilar to each other are concurrently purchased. 
     An example of first sales data will be described by using an example of sales data illustrated in  FIG. 7  described above. The example of sales data illustrated in  FIG. 7  is data obtained by further adding information relating to a transaction with basket number “1000003” to the example of sales data illustrated in  FIG. 2 . 
     In this example, when it is assumed that a first product is Product 1 and a second product is Product 4, Product 1, which is the first product, is purchased in both pieces of information relating to two transactions with basket numbers “1000001” and “1000002” included in sales data. Meanwhile, in the transaction with basket number “1000001”, Product 4, which is the second product, is purchased. Accordingly, in this example, the second product data exclusion unit  241  derives only information relating to the transaction with basket number “1000002” as first sales data. 
     In addition, in this case, Product 4, which is the second product, is purchased in both pieces of information relating to two transactions with basket numbers “1000002” and “1000003” included in sales data. Meanwhile, in the transaction with basket number “1000001”, Product 1, which is the first product, is purchased. Accordingly, in this example, the first product data exclusion unit  242  derives only information relating to the transaction with basket number “1000003” as second sales data. 
     Note that other components included in the analysis device  100  according to the present example embodiment have configurations which operate almost similarly as the corresponding components in the analysis device  100  according to the first example embodiment, except for the above-described contents. 
     For example, in the example in  FIG. 2 , the first concurrent sales quantity derivation unit  111  derives a first concurrent sales quantity as 1 for Product 1, 2 for Product 2, and 2 for Product 3, respectively. In addition, in the example in  FIG. 7 , the first concurrent sales vector derivation unit  121  derives a first concurrent sales vector as (2, 2). The second concurrent sales quantity derivation unit  121  and the second concurrent sales vector  122  operate similarly as the first concurrent sales quantity derivation unit  111  and the first concurrent sales vector  121  respectively. For example, in the example in  FIG. 7 , the second concurrent sales vector  122  derives a first concurrent sales vector as (1, 2). The similarity derivation unit  130  derives similarity, as with the similarity derivation unit  130  according to the first example embodiment. By using the first sales data and the second sales data described above, the similarity derivation unit  130  is able to accurately derive similarity between products that are dissimilar to each other. 
     Subsequently, an example of an operation of the analysis device  200  according to the second example embodiment of the present invention will be described by using a flowchart illustrated in  FIG. 8 . 
     First, the second product data exclusion unit  241  derives first sales data, based on sales data relating to a first product (Step S 201 ). Subsequently, the first concurrent sales quantity derivation unit  111  derives a first concurrent sales quantity, based on the first sales data derived at Step S 201  (Step S 202 ). Next, the first concurrent sales vector derivation unit  121  derives a first concurrent sales vector, based on the first concurrent sales quantity derived at Step S 202  (Step S 203 ). 
     Along with the operations of Steps S 201  to S 203 , the first product data exclusion unit  242  derives second sales data from sales data, based on sales data relating to a second product (Step S 204 ). The second concurrent sales quantity derivation unit  112  derives a second concurrent sales quantity (Step S 205 ). In addition, subsequent to the process of Step S 205 , the second concurrent sales vector derivation unit  122  derives a second concurrent sales vector (Step S 206 ). As an example, the processes of Steps S 204  to S 206  are performed similarly as the processes of Step S 201  and Step S 203  respectively. 
     Note that the flowchart illustrated in  FIG. 8  indicates that the operations of Steps S 201  to S 203  and the operations of Steps S 204  to S 206  are concurrently performed. However, these operations may be performed in different order from that in the example illustrated in  FIG. 8 , similarly to the operation of the analysis device  100  according to the first example embodiment. 
     Finally, the similarity derivation unit  130  derives similarity between the first product and the second product (Step S 207 ). The similarity between the first product and the second product derived by the similarity derivation unit  130  is output from, for example, any kinds of output means, similarly to the analysis device  100  according to the first example embodiment. In addition, the similarity between the first product and the second product derived by the similarity derivation unit  130  may be stored in any kinds of storage means. 
     As described above, the analysis device  200  according to the second example embodiment of the present invention derives first sales data and second sales data by using the second product data exclusion unit  241  and the first product data exclusion unit  242 , respectively. Then, the analysis device  200  according to the second example embodiment of the present invention derives similarity between a first product and a second product, based on the first sales data and the second sales data. 
     As described above, products that are frequently purchased concurrently at a time may include products that are dissimilar to each other. The analysis device  200  according to the present example embodiment derives similarity by excluding sales data in which a first product and a second product, which are targets for deriving similarity, are concurrently purchased. In other words, the analysis device  200  according to the example embodiment derives similarity by excluding a case where there is a possibility that two products are concurrently purchased because of the dissimilarity. Therefore, the analysis device  200  according to the present example embodiment is able to derive similarity between products more accurately, in comparison with the analysis device  100  according to the first example embodiment. 
     Modification Example of Second Example Embodiment 
     There are various modification examples conceivable as the analysis device  200  according to the present example embodiment, similarly to the analysis device  100  according to the first example embodiment. 
     As a modification example, the analysis device  200  according to the present example embodiment may include a means for acquiring and managing sales data, similarly to the modification example of the analysis device  100  according to the first example embodiment.  FIG. 9  is a diagram illustrating a configuration of an analysis device  200 A according to the modification example. 
     The analysis device  200 A illustrated in  FIG. 9  further includes, on the analysis device  200  according to the present example embodiment, a sales data management unit  150 , a sales data storage unit  160 , a first product data selection unit  171 , and a second product data selection unit  172 . The respective components are made the same as the corresponding components in the modification example of the analysis device  100  according to the first example embodiment. 
     The second product data exclusion unit  241  and the first product data exclusion unit  242  may be configured as a single product data exclusion unit. In this case, the product data exclusion unit derives sales data or second sales data as appropriate in accordance with input information. 
     Besides the above, various configurations indicated as the modification example of the first example embodiment of the present invention can also be applied to the second example embodiment of the present invention as appropriate. 
     Third Example Embodiment 
     Next, a third example embodiment of the present invention will be described.  FIG. 10  is a diagram illustrating a configuration of an analysis system according to the third example embodiment of the present invention. 
     As illustrated in  FIG. 10 , an analysis system  30  according to the third example embodiment of the present invention includes an analysis device  300  and an analysis unit  301 . The analysis device  300  is the analysis device described in the first example embodiment or the second example embodiment of the present invention, or the various modification examples thereof. The analysis unit  301  derives a similarity relation with respect to a plurality of products, based on similarity derived by the analysis device  300  regarding respective combinations of the plurality of products. 
     In the present example embodiment, the analysis device  300  derives similarity between two products, which are a first product and a second product, as in the description relating to the first example embodiment or the second example embodiment of the present invention. Meanwhile, in product planning and sales, grasping an overall relation of similarity relating to products which belong to a category as an analysis target may be required. 
     In the present example embodiment, the analysis unit  301  derives a similarity relation with respect to a plurality of products, by analyzing, for example, a relation of similarity derived by the analysis device  300  regarding between respective combinations of the plurality of products. Since the analysis device  300  is the first example embodiment or the second example embodiment of the present invention, the analysis device  300  derives similarity between two products more accurately. Accordingly, the analysis system  30  according to the present example embodiment is able to derive a more accurate relation of similarity relating to a plurality of products. 
     The analysis unit  301  visualizes and derives a relation of similarity relating to all of a plurality of products, by using, for example, a method such as cluster analysis, and multidimensional scaling method. In this case, an overall relation of similarity relating to a plurality of products can be grasped quantitatively and visually, based on a result derived by the analysis system  30 . 
     The analysis unit  301  may derive a plurality of similarity relations by analyzing a relation of similarity relating to respective combinations of the plurality of products, by using any method different from the above methods. In the both cases, an overall similarity relation with respect to a plurality of products can be grasped quantitatively, by using a relation of similarity with respect to the plurality of products derived by the analysis system  30  according to the present example embodiment. 
     Note that a relation of similarity derived by the analysis unit  301  is output from a display device, any kinds of output means including a communication network, etc. In addition, similarity between a first product and a second product derived by the analysis unit  301  may be stored in any kinds of storage means in such that it can be referred when needed. 
     In addition, a result with respect to a similarity relation derived by the analysis unit  301  is used for various purposes. As an example, the result relating to the similarity relation is applied to, in product planning, a purpose for searching an area where there are fewer similar rival products for differentiation of a new product. 
     As another example, the result with respect to the similarity relation is used for a use for preventing a situation where stockout of a product leads to loss of sales in a store. In this case, by using the result with respect to the similarity relation, it becomes possible to stock a similar product in a larger amount when there is a conceivable possibility of occurrence of stockout, so as to allow a customer to purchase the similar product even in a case of stockout. 
     In the above, the invention of the present application has been described with reference to the example embodiments. However, the invention of the present application is not limited to the above-described example embodiments. Various modifications which can be understood by those skilled in the art can be made to the configurations and details of the invention of the present application within the scope of the invention of the present application. In addition, the configurations in the respective example embodiments can be combined with one another within a range not departing from the scope of the present invention. 
     This application is based upon and claims the benefit of priority from Japanese Patent Application No. 2015-40894, filed on Mar. 3, 2015, the disclosure of which is incorporated herein in its entirety. 
     REFERENCE SIGNS LIST 
     
         
           30  Analysis system 
           100 ,  100 A,  200 ,  200 A,  300  Analysis device 
           111  First concurrent sales quantity derivation unit 
           112  Second concurrent sales quantity derivation unit 
           121  First concurrent sales vector derivation unit 
           122  Second concurrent sales vector derivation unit 
           130  Similarity derivation unit 
           241  Second product data exclusion unit 
           242  First product data exclusion unit 
           150  Sales data management unit 
           160  Sales data storage unit 
           171  First product data selection unit 
           172  Second product data selection unit 
           301  Analysis unit