Efficient Mining of Frequent Itemsets and a Measure of Interest for Association Rule Mining

Abstract

Association rule mining is an important research topic in data mining. Association rule mining consists of two steps: finding frequent itemsets and then extracting interesting rules from the frequent itemsets. In the first step, efficiency is important since discovering frequent itemsets is computationally time consuming. In the second step, unbiased assessment is important for good decision making. In this paper, we deal with both the efficiency of the mining algorithm and the measure of interest of the resulting rules. First, we present an algorithm for finding frequent itemsets that uses a vertical database. We also introduce a modified vertical data format to reduce the size of the database and an itemset reordering strategy to reduce the size of the intermediate tidsets. Second, we present a new measure to evaluate the interest of the resulting association rules. Our performance analysis shows that our proposed algorithm reduces the size of the intermediate tidsets that are generated during the mining process. The smaller tidsets make intersection operations faster. Using our interest-measuring test helps to avoid the discovery of misleading rules.

QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules

“…Moreover, the 506 rule sets obtained have a low number of attributes, giving the advantage of easier understanding from a user'used nonparametric tests for multiple comparison to find the best approach. As in Section 4 3,. we applied the 510 statistical tests to the average results obtained by the analyzed algorithms over 22 real-datasets for the interestingness mea-511 sures lift, CF and netconf, which are available in Appendix A.…”

“…When the domain is continuous, the association rules are known as QARs, 91 in which each item is a pair attribute-interval. For instance, a QAR could be Age 2 [30,52] and Salary 2 [3,4] ? NumCars 92 2 [3,4].…”

“…For instance, a QAR could be Age 2 [30,52] and Salary 2 [3,4] ? NumCars 92 2 [3,4]. 93 Support and Confidence are the most common measures to assess association rules.…”

“…Conviction for a rule X ? Y is defined as:The netconf[3] measure evaluates the interest of the association rules, which is based on the support of the rule and its 144 antecedent and consequent support. As with the CF measure, the netconf can detect misleading rules that are not detected by EAs simultaneously deal with a set of possible solutions (the so-called population) which enables them to find several 152 members of the Pareto optimal set in a single run of the algorithm.…”

Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

“…In addition to the appropriate coverage and reliability, the obtained rules should also be interesting and comprehensible. Ahn and Kim [49] presented an interest measure called netconf to evaluate the interestingness of the association rules. The netconf for a rule X →Y is defined as…”