High Utility Itemset mining using UP growth with Genetic Algorithm from OLAP system

Frequent pattern mining play an important role for analysing vast amount of data in data mining. In practice, Frequent pattern mining cannot meet the challenges of real world problems due to items differ in various. Hence an emerging technique called Utility-based data mining is used in data mining processes.The utility mining not only considers the frequency but also see the utility associated with the itemsets.The main objective of utility mining is to extract the itemsets with high utilities, by considering user preferences such as profit,quantity and cost. In our proposed approach, we are using UP growth with Genetic Algorithm. The idea is that UP growth algorithm would generate Potetially High Utility Itemsets and Genetic Algorithm would optimize and provide the High Utility Item set from it. On comparing with existing algorithm, the proposed approach is performing better in terms of execution time. Keywords—Utility mining, High utility itemsets, UP Growth, Genetic Algorithm, Frequent itemset mining. I.INTRODUCTION A. Data Mining Data Mining refers to extracting or mining knowledge from large databases. Data mining and knowledge discovery in the databases is a new interdisciplinary field, merging ideas from statistics, machine learning, databases and parallel computing. Hence Data mining can be defined as: a)non trivial extraction of implicit, previously unknown and potentially useful information form the large databases, b)the search for the relationships and global patterns that exists in large databases but are hidden among vast amounts of data, c)refers to using a variety of techniques to identify nuggets of information or decision –making knowledge in the database and extracting these in such a way that they can be put to use in areas such as decision support , prediction, forecasting and estimation, d)it is the system self learns from the previous history of investigated system , formulating and testing hypothesis about rules which system works properly ,e)the process of discovering meaningful, new correlation pattern and trends by shifting through large amount of data stored in repositories , using pattern recognition techniques as well as statistical and mathematical techniques. [1] For the past two decades data mining has emerged as an important research area . This is mainly due to the interdisciplinary nature of the subject and the diverse range of application domains in which data mining based products and techniques are being employed. This includes bioinformatics, genetics, medicine, clinical research, education, retail and marketing research. Data mining has been considerably used in the analysis of customer transactions in retail research where it is termed as market basket analysis. Market Basket Analysis is the process of exploring customer buying habits by finding associates between the different items that customers place in their “Shopping Baskets”. The discovery of such associations can help retailers develop marketing strategies by gaining insight into which items are frequently purchased together by customer. B. Frequent Itemset Mining Frequent itemset mining is the mining of frequent itemsets (set of items) from transactional or relational data sets. An itemset can be defined as a non-empty set of items. An itemset with k different items is termed as a k-itemset. For e.g. {bread, butter, milk } may denote a 3-itemset in a supermarket transaction . The notion of frequent itemsets was introduced by Agrawal et al [2]. Frequent itemsets are the itemsets that appear frequently in the transactions. The goal of frequent itemset mining is to identify all the itemsets in a transaction dataset [3]. Frequent itemset mining plays an essential role in the theory and practice of many important data mining tasks, such as mining association rules [2,4,5], long patterns ,emerging patterns, and dependency rules. It has been applied in the field of telecommunications, census analysis and text analysis [6]. The criterion of being frequent is expressed in terms of support value of the itemsets. The Support value of an itemset is the percentage of transactions that contain the itemset. A Saranya et al, Int.J.Computer Technology & Applications,Vol 6 (2),235-239 IJCTA | Mar-Apr 2015 Available online@www.ijcta. com 235 ISSN:2229-6093