Generating a Condensed Representation for Positive and Negative Association Rules

Given a large collection of transactions containing items, a basic common association rules problem is the huge size of the extracted rule set. Pruning uninteresting and redundant association rules is a promising approach to solve this problem. In this paper, we propose a Condensed Representation for Positive and Negative Association Rules representing non-redundant rules for both exact and approximate association rules based on the sets of frequent generator itemsets, frequent closed itemsets, maximal frequent itemsets, and minimal infrequent itemsets in database B. Experiments on dense (highly-correlated) databases show a significant reduction of the size of extracted association rule set in database B.

A novel interestingness measure based on fusion model for association rules mining

Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules.

A Parallel Algorithm of Association Rules Applicable for Sales Data Analysis

Background: This paper puts forward a parallel algorithm of association rules applicable for sales data analysis based on association rules by utilizing the idea of division and designs a sales management system for mall including behavior recognition and data analysis function as the application model of this algorithm with clothing store data management system as study object. Objective: To adapt to the data particularity of the study object, while mining the association rules, the improved algorithm also considers the priority relations, weight, negative association rules, and other factors among different items of the database. Method: His improved algorithm is applied to Apriori algorithm, dividing the original database into n local data sets, mining the local data sets parallelly, finding out the local frequent data sets in each local data set, and finally counting the support and determine the final overall frequent sets. Result: Experiment verifies that this algorithm reduces the visit times of the database, shortens the mining time of algorithm, and improves the effectiveness and adaptability of the mining result. Conclusion: With the application with negative association rules added, data with diversified results can be mined during analyzing specific problems, mining efficiency is improved, the accuracy and adaptability of mining result is guaranteed, and the high efficiency of algorithm is also ensured. The improvement of increment mining efficiency of database will be considered next while the database is updated continuously.