dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodology Methods Variables of interest were derived from a systematic review of the important literature and analyzed through a generalized linear model to determine the suitability of geo-sampled normalized predictors to our geo-referenced measles unvaccinated outcome variable. Model selection for our response variable (measles non-vaccination) was based on Akaike information criterion (AIC) and Pseudo R square value (R²) in SAS 9. 4. Similarly, multicollinearity in the independent variables was excluded to preserve homoscedascity using variance inflation factor (<10) for our model in PROC REG. We utilized Poisson regression model for the Nigerian DHS 2013 dataset, where the non-vaccinated measles count was the dependent variable on the Left-hand side of the equation and the right-hand side of the equation contained the predictor variables (literacy, wealth index, religion).
The Poisson model follows the regression equation illustrated in  [16] : 2 2 2 ˆ( ) ( ) ( ) i i i i y y y y y y -= - + - ∑ ∑ ∑ Which was further rewritten as SST=SSM+SSE, where SS was notation for sum of squares and T, M, and E in PROC GEN MOD were the descriptions for total quantized model error estimates. The squares of the sample correlation (r 2 ) given as the ratio of SSM/SST was the proportion of variability for the geo-sampled, normalized measles non-vaccinated dataset  [16]. Likewise, the r value was the strength of association between measles non-vaccinated count i. e. , (y i ) to the fitted values i. e. , ˆl y In addition, we applied a negative binomial regression due to the presence of extra-Poissonial variations observed within in the Poisson regression model. The Poisson distribution is a special case of the negative binomial distribution where the mean approximates the standard deviation  [9].
The negative binomial distribution reduced the extra-Poissonial variation by collapsing the outliers to the mean: in so doing, the variance was equal to the mean  [17]. In the linear regression analyses, of the geo-sampled, measles unvaccinated related, normalized dataset k; the null hypothesis was H 0 : k=0 and the alternative hypothesis was H a : k>0. The log-likelihood (i. e. , LL) for the models were noted. We employed the likelihood ratio (LR) test to compute the LR statistic using -2(LL) (Poisson) and the LL (i. e. , negative binomial) as reported in (http://www. ats. ucla. edu/). Furthermore, we applied PROC AUTOREG as a form of spatial error (SE) model in evaluating all probabilistic residual estimates from the measles unvaccinated model. The spatial error, measles unvaccinated epidemiologic model treats spatial dependency primarily as a nuisance; in the same way, statistical approaches often treat temporal serial correlation as a noise to be eliminated  [10, 18].
These are the Yule-Walker estimates  [19]. We utilized the mathematical concept of Kalman filter algorithm utilized by Jacob et al. [17]  in transforming less precise measurements or random variations to more accurate estimates of unknown variables instead of those based on single measurement. Kalman filters algorithm is a set of mathematical equations that allows for data transformation through the inverse Cholesky root of υ and subsequently compute the GLS output through an iterative process. Additionally, we also applied PROC AUTOREG for Durbin Watson statistics to detect inherent residual error coefficients in the regression analysis for measles unvaccinated related data. The DWPROB option in PROC AUTOREG allows for the determination of significance level within our predictor variables.
Durbin Watson d Values <2 indicates a positive serial correlation and d>2 signifies successive error terms are on average are much different from one another i. e. negatively correlated  [20]. Pearson product moment correlation coefficient is a form of spatial autocorrelation method, employed in this research to quantitate geo-referenced unsupervised predicative LULC urban and non-urban geo-classified explanatories in AUTOREG. We constructed a PROC VARIOGRAM in SAS 9. 4 to generate a scatter plot of standardized versus summed nearby standardized georeferenced measles unvaccinated related neighborhoods on geoclassified LULC maps. In so doing, the geographical distribution of geo-referenced measles unvaccinated related cluster units and their associated co-variates was cartographically delineated on a geoclassified LULC maps in ArcGIS.
[23]. Likewise, j 1, 1 2 (x X) 1 n J J i s n = ≠ - = - ∑ where n is the total number of features (2. 2) As such, a geo-referenced dataset of measles related primary sampling unit in Nigeria was digitally overlaid onto sub-meter geoclassified google earth data. Initially, we generated an unsupervised LULC classification in ArcGIS using urban and non-urban polygon data. We considered a linearized model framework for a regressively determining statistically significant landscape co-variates associated with each polygon using ArcGIS geo-classification and then constructed a Moran's I spatial autocorrelation measles unvaccinated primary sampling unit model.
Data description Demographic Health survey 2013 for Nigeria was drawn from primary sampling units referred to as clusters chosen from enumeration areas for the survey. A total of 40,680 households were chosen from 904 clusters within urban and non-urban zones of the country. This primary sampling frame/cluster or neighborhood is based on the population and housing census conducted in Nigeria in 2006  [24]. However, the geographic coordinates of sampled households were displaced by 0-2 km of positional error in the urban and 0-5 km in the rural zones, with 1% of the rural clusters further displaced randomly by a maximum of 10 km to protect the confidentiality of the respondents. Urban zones are often taken as population estimate of more than 20,000 individuals whose economic activity were non-agrarian  [24].
9%) vis à vis the population of children without vitamin A supplement (79. 1%). Likewise, among the religious groups in Nigeria, Christianity had a lower proportion of measles related non-vaccination (41. 6%) in relation to Islam (74. 9%) and Traditionalist (79. 0%). Additionally, higher proportion of nonvaccination was noticed among the Fulani (85. 7%) and Hausa (79. 7%) ethnic populations of northern Nigeria when compared to Igbo (36. 4%), Yoruba (37%) in the and others (55. 6%) (Table  1 ). PROC GENMOD with a Poisson distribution in SAS 9. 4 was utilized in analyzing suitable predictors of interest based on literature reviews to our outcome variable and the LINK function was log (Table  2 ). We indicated the SCALE OPTION; although, it's a default in SAS 9. 4 and enables the researcher to appreciate the effect of scaled Pearson chisquare on over dispersion (Table  3 ).
The results obtained, satisfied the significant level of <0. 05 for most of our regressors; except for distance (P-value=0. 51) in rural, literacy in urban zone (P-value=0. 782) and Hausa within the ethnicity group of the rural zone (P-value=0. 713) as reported in Table  2  below. The goodness of fit assessment comes as part of diagnostic output generated in PROC GENMOD. The deviance value divided by the degree of freedom is used as an indicator of model dispersion in this study. A value <1 is taken as under-dispersed while values >1 is taken as an over-dispersed dataset. The goodness of fit assessment of our Poisson model showed an over-dispersion of the experimental dataset by a factor of 5. 3 and 3. 4 respectively for urban and rural zones within the Nigerian study site (Table  3  correlation within the models for rural (0.
Our research analysis demonstrated that the first order correlation tests confirms the assumption of positive spatial autocorrelation in the geo-referenced measles vaccination related dataset. Furthermore, we applied PROC VARIOGRAM procedure with The LAGDISTANCE OPTION for the computation of autocorrelation index. The LAGDISTANCE in the autocorrelation model signifies the neighborhood size. Likewise, the binary row-averaged weights created for the normalized, asymptotic, epidemiologic measles unvaccinated measles related dataset within the spatial dependence procedure generates a Moran's coefficient equivalent to the regression slope of Moran's scatter plot  [15]  (Table  6 ). The Moran's index and the Geary's c ratio derived from PROC VARIOGRAM revealed a positive spatial autocorrelation for rural (MI=0.
The dispersion parameter values for rural (0. 041) and urban (0. 125) in the statistical output demonstrated that the negative binomial model was justifiable in correcting for over-dispersion. Durbin Watson test allowed us to identify the inherent structural flaw in the model in urban zones (PSC=0. 166) and rural agro-pasture lands (PSC=0. 273) as seen in other seasonal, time series dataset. However, a Durbin Watson test <1 may signify that the values for successive error terms are close in value to one another and dependent. In this case, we have serially correlated positive errors and the DW test statistics was applied to elucidatively quantitate the distance lag between centroids points within neighborhoods at the Nigerian study site. Similarly, the Durbin t statistics at the first lag (lag1) demonstrated a positive serial autocorrelation with our autoregressive model for urban (Durbin's t=1.
Differentiating Non-Homoscedasticity and Geospatially Extreme Outliers for Urban and Rural Landscape Dataset Using Pearson's Product Moment Correlation Coefficients for Quantitating Clustering Tendencies in Non-Vaccinated Measles Populations in Nigeria Samuel Alao, Komi Mati and Benjamin Jacob* Department of Global Health, College of Public Health, University of South Florida, Tampa, FL, USA.
Alao S, Mati K, Jacob B (2016) Differentiating Non-Homoscedasticity and Geospatially Extreme Outliers for Urban and Rural Landscape Dataset Using Pearson's Product Moment Correlation Coefficients for Quantitating Clustering Tendencies in Non-Vaccinated Measles Populations in Nigeria. J Remote Sensing & GIS 5: 185. doi: 10. 4182/2469-4134. 1000185.
The first-order measles unvaccinated related parameter estimates with a lag dependent variable. Alao S, Mati K, Jacob B (2016) Differentiating Non-Homoscedasticity and Geospatially Extreme Outliers for Urban and Rural Landscape Dataset Using Pearson's Product Moment Correlation Coefficients for Quantitating Clustering Tendencies in Non-Vaccinated Measles Populations in Nigeria. J Remote Sensing & GIS 5: 185. doi: 10. 4182/2469-4134. 1000185.
Spatial statistics derived from PROC VARIOGRAM SAS output. Alao S, Mati K, Jacob B (2016) Differentiating Non-Homoscedasticity and Geospatially Extreme Outliers for Urban and Rural Landscape Dataset Using Pearson's Product Moment Correlation Coefficients for Quantitating Clustering Tendencies in Non-Vaccinated Measles Populations in Nigeria. J Remote Sensing & GIS 5: 185. doi: 10. 4182/2469-4134. 1000185.
Alao S, Mati K, Jacob B (2016) Differentiating Non-Homoscedasticity and Geospatially Extreme Outliers for Urban and Rural Landscape Dataset Using Pearson's Product Moment Correlation Coefficients for Quantitating Clustering Tendencies in Non-Vaccinated Measles Populations in Nigeria. J Remote Sensing & GIS 5: 185. doi: 10. 4182/2469-4134. 1000185 Page 4 of 7 Citation: Volume 5 • Issue 4 • 1000185 J Remote Sensing & GIS, an open access journal ISSN: 2469-4134.
High utility itemset mining (HUIM) plays an important role in the data mining community and in a wide range of applications. For example, in retail business it is used for finding sets of sold products that give high profit, low cost, etc. These itemsets can help improve marketing strategies, make promotions/ advertisements, etc. However, since HUIM only considers utility values of items/itemsets, it may not be sufficient to observe product-buying behavior of customers such as information related to "regular purchases of sets of products having a high profit margin". To address this issue, the occurrence behavior of itemsets (in the term of regularity) simultaneously with their utility values was investigated. Then, the problem of mining high utility itemsets with regular occurrence (MHUIR) to find sets of co-occurrence items with high utility values and regular occurrence in a database was considered.
Chan, et al. [4]  proposed to consider the importance of items, such as profit, cost and other user-defined factors as well as the quantity of occurrence (e. g. units of a product bought by each customer). Based on these two components, an itemset is called a high utility itemset (HUI) if its utility (i. e. unit profit  quantity of occurrence) is no less than a user-assigned minimum utility threshold ( ); otherwise, it is called a low utility itemset (LUI). Mining HUI has a wide range of applications, such as crossmarketing in retail, web clickstream analysis, biomedical applications and mobile commerce. Although mining HUI can discover interesting sets of items with high utility values, it may not be sufficient to analyze interesting buying behaviors of customers. For example, as shown in Table  1  (containing per-unit-profits of items) and Table  2  (a transactional database with quantity of occurrence), it can be seen that item ' ' occurs in transactions and with 20 and 1 pieces bought with a profit of 15$ per unit sold.
These itemsets can produce information related to "regular purchases of sets of products with a high profit margin" and can help business know customers' demand, manage inventory, develop new promotions/marketing strategies, and so on. To mine high utility itemsets with regular occurrence (HUIRs), an efficient single-pass algorithm named MHUIRA is proposed. MHUIRA avoids repeatedly scanning the database by employing a simple list to maintain the utility information of each transaction. Furthermore, the concepts of transaction-weighted utility  [6] , tight over-estimated utility  [7]  are applied early -in order to remove low utility itemsets out of consideration -and a utility list, called UL, is applied for maintaining utility and occurrence information of each itemset (as in  [5] ). Moreover, a new modified utility list structure (also called NUL for short) was designed in this extended version to increase the efficiency of the computing of the itemsets.
High Utility Itemset Mining (HUIM) Since Chan  [4]  first proposed high utility itemset mining, many studies have been conducted to improve performance of the mining process. Liu  [6]  proposed a two-phase algorithm with an overestimated utility strategy named transaction-weighted utility (TWU) used for pruning the search space. A TWU of an itemset is an upper bound of its utility, which keeps the downward closure property  [2]. Based on TWU, Lin  [8]  proposed the high utility pattern tree (HUP-tree) algorithm for mining high utility itemsets without candidate generation. In  [9] , a pattern growth approach named UP-growth was proposed for mining high utility itemsets within two scans of a database. Up-growth applies four effective strategies, i. e. DGU, DGN, DLU and DLN to prune candidates during the mining process.
Liu  [7]  proposed the HUI-miner algorithm with a tight overestimated utility strategy, which can estimate utility values close to the actual utility of itemsets. Tight overestimated utility is used for reducing the search space. High utility itemset mining has also been extended to several other aspects. Wu  [10]  proposed three efficient algorithms to mine closed+ high utility itemsets, a concise representation of high utility itemsets. HUIM on incremental and modification databases and on data streams is proposed in  [11] [12] [13] [14]. Negative unit profits of items are addressed in  [15, 16]. To avoid difficulties in setting a proper utility threshold, in  [17] [18] [19]  attempts were done to mine a set of itemsets with the highest utility. Podpecan  [20]  and Sugunadevi  [21]  proposed to discover high utility-frequent itemsets based on consideration of utility and frequency of occurrence.
Frequent-Regular Itemset Mining (FRIM) Tanbeer  [3]  has proposed the problem of frequent-regular itemset mining (FRIM) in order to observe occurrence behavior of itemsets in terms of frequency and regularity of occurrence. A set of itemsets with high frequency and regular occurrence that meets user-given support and regular thresholds is generated. FRIM can be applied in a wide range of applications, such as genetic and medical data analysis  [22] [23] [24] , manufacturing  [25] , behavior of moving objects analysis  [26]  and game player behavior  [27]. In addition, many studies have proposed to extend the framework of FRIM in several ways. We here give a brief review of the main related works. Attempts to mine frequent-regular itemsets from incremental database/data streams based on the use of a tree-based structure, sliding window, and/or vertical data format are reported in  [28, 29].
Utility of Items/Itemsets Example 1. See Tables  1  and 2  for the external utility value of items and a transactional database containing 8 transactions with internal utilities. The utility of item ' ' can be calculated as , , , , , 3 3 2 3 2 3 5 3 3 3 45. Based on the three definitions above, an itemset is a high utility itemset if its utility is not smaller than a user-specified utility threshold ( ). The problem of high utility itemset mining is the task of finding itemsets whose utility is not smaller than. The main challenge of this task is the large size of the search space due to the downward closure property  [2] , which cannot be held, i. e. a superset of a low utility itemset can be a high-utility one. Therefore, the concept of Transaction-Weighted Utility (TWU)  [9] , an over-estimated utility of an itemset that meets the downward closure property, is applied, which is defined as follows.
Regularity of Occurrence The concept of regularity is introduced to observe the occurrence behavior of itemsets in terms of regularity of occurrence. The regularity of an itemset is related to the gap of consecutive transactions in which the itemset does not occur in the database, which can be described as follows. Definition 9. The regularity of before its first occurrence in transaction is the gap of absence of between the first transaction in the database and the first occurrence of in transaction , defined as ,. Definition 10. The regularity of between two consecutive occurrences of in transactions and is the gap of occurrence of X between and , defined as , ,. Definition 11. The regularity of after its last occurrence in transaction is the gap of absence from the last occurrence of in to the last transaction of database, defined as ,.
Definition 12. The regularity value of in a database is the maximal gap of absence based on the occurrence of in database , defined as , , For the transactional database in Table  2 , the regularity of item ' ' can be calculated as , , , With the regularity value of an itemset (i. e. ), we can say that occurs at least once in every consecutive transactions or never disappears from the database more than consecutive transactions. Problem Statement. Given a database , a utility threshold , and a regularity threshold , the task of mining high utility itemsets with regular occurrence is to find the complete set of itemsets: (i) whose utility values are not smaller than (i. e. itemsets giving profit ); and (ii) whose regularity values are not greater than (i. e. itemsets must occur at least once in consecutive transactions).
Utility List and New Utility List Structure As proposed by Liu  [7] , a utility list of an itemset is an ordered set of 3-tuple entries used for maintaining utility and occurrence information of , defined as 1 , , 1 1 , , 1 1 , 2 , , 2 2 , , 2 2 , … , , , , ,. where each entry of contains three pieces of information, i. e. (i) -a TID of transaction that contains ; (ii) , -the utility value of in transaction ; and (iii) , -the remaining utility of in transaction , respectively. Example 5. Let's consider item ' ' with external utility 3 (Table  1 ). Its occurrence in the database from Table  2 which is not correct. To alleviate this difficulty, Liu  [7]  proposed to repeatedly decrease each utility value , in by , of. However, this consumes computational time. From the above issue, we focus here on avoiding the repeated utility calculation process.
Thus, the utility list is modified by adding a new piece of information, utility of prefix items, into each entry of the utility list, where each entry of the new modified utility list (NUL) is in the form of , , , , where is the new additional information that expresses the utility value of prefix items. Example 6. With the transactional database from Tables  1  and 2 , itemset ' ' has 1,9,73,0 , and item ' ' has as 2,10,0,0 , 5,6,13, 0 , 7,2,40,0 , 8,2,39, 0 , respectively. Since both items do not have a prefix, we can easily compute the utility of itemset ' ' from the intersection of and. The itemset ' ' first occurs in transaction , then the third entry , respectively. Thus, the entry of is 5,12,13,6. Also, this computation can be applied to other occurrences of itemsets ' '. Then, each entry of contains the correct utility of itemset ' '.
MHUIRA Algorithm As mentioned above, MHUIRA not only applies the new utility list structure in order to efficiently maintain occurrence information and utility values but also employs the concept of remaining and overestimated utilities to cut down the search space. MHUIRA avoids repeatedly scanning the database by creating a simple list named for storing the transaction utility of transactions in the database (used for computing the remaining utility of all items). MHUIRA consists of two steps: (i) 1-HUIRs identification -the task of database scanning to capture occurrence information of each item into a simple 2D list named , where each 1-dimension, e. g. 1 , 2 ,. , of , is used for maintaining 1-itemsets, 2-itemsets, …, -itemsets, respectively; and (ii) Mining HUIRs -the process of mining a complete set of HUIRs from the itemsets contained in.
Algorithm 1. 1-HUIR identification Input. : transactional database, : a utility threshold, : a regularity threshold Output. : a 2D list containing items with potentially to be HUIR in 1 Itemset is then collected in the set of HUIRs if the utility of is not smaller than the utility threshold. Next, MHUIRA repeats the same process of merging item with another item in 1. At the end of the merging of item , 2 contains 2-itemsets having: (i) item as a prefix of itemset and (ii) regularity not greater than the regularity threshold, respectively. In addition, if 2 contains more than one itemset, then 3 is created and initialized. In a similar manner, each itemset in 2 is then merged with another itemset in 2 in order to generate 3-itemset. This process is then continued until is empty or contains only one itemset.
Example of MHUIRA with NU Let's consider a table of items' external utilities and a table of a transactional database with internal utilities (see Tables  1  and 2 ). Assume that the regularity and utility thresholds are set to be 3 and 30. The task of mining a complete set of HUIRs by MHUIRA with NUL is to find itemsets having regularity not greater than 3 and utility not smaller than 30, respectively. As shown in Fig. 1 , MHUIRA first creates with 8 entries for transaction and in which 1 contains 8 entries for items , , … ,. Next, each transaction is read. For the first transaction, its transaction utility is computed (i. e. 3 3 8 1 2 30 1 5 82) and then added into the first entry of. In addition, entries of item , , , and in 1 are updated. Notice that each entry in 1 contains item name, regularity, utility, remaining utility, and NUL, respectively.