Abstract
Many association rule mining algorithms have been well-established, such as Apriori, Eclat, FP-Growth, or LCM algorithms. However, the challenge is that the huge size of association rules is extracted by using these algorithms, and it is difficult for users to select satisfied association rules from them. In this paper, a new method is proposed to select satisfied association rules, which is based on the aggregation of fuzzy linguistic satisfied degrees of extracted association rules. To this end, two problems must be solved, one is which interesting measures are utilized to obtain fuzzy linguistic satisfied degrees of association rules and the other is how to aggregate them. For the first problem, many objective and subjective interesting measures have been proposed, which are generally included in [0, 1] or others universes and easily calculated by support, confidence, or other measures, these interesting measures cannot be directly aggregated, because different interesting measures represent different satisfied degrees of association rules. In this paper, a new transformation function is proposed to transform these interesting measures into fuzzy linguistic satisfied degrees, such as dissatisfied, fair, satisfied, and so on. For the second problem, by considering different weights of objective and subjective interesting measures, linguistic aggregation operators are designed to aggregate these fuzzy linguistic satisfied degrees of association rules. Accordingly, satisfied association rules are selected by using order on the aggregation results of linguistic satisfied degrees. In cases' study, Apriori, Eclat, FP-Growth, and LCM algorithms are first utilized to extract association rules with higher support or confidence measures from Chess, Connect, Mushroom, and T40I10D100K databases, then the proposed method is applied to obtain fuzzy linguistic satisfied degrees of extracted association rules and aggregate and select satisfied association rules from those extracted association rules, and comparison and analysis show that the proposed method is a useful and alternative tool to select satisfied association rules from extracted association rules.
Highlights
After Agrawal proposed association rule mining [1], it has become one of the most popular data mining techniques and contributed to many advances in the area of knowledge discovery, by which implicit, previously unknown and potentially useful knowledge can be discovered from large datasets
Major contributions of the paper are summarized as follows: 1) Transform selection of satisfied association rules into a decision making problem, where criteria are objective and subjective interesting measures, which are calculated by support measure, confidence measure or structure of extracted association rule, alternatives are the set of extracted association rules which are generated by existed association rule mining algorithms; 2) Present a new transformation function to transform objective and subjective interesting measures of extracted association rules into fuzzy linguistic satisfied degrees
AGGREGATION LINGUISTIC SATISFIED DEGREES AND SELECTION OF SATISFIED ASSOCIATION RULES based on Table 3 and weights of objective and subjective interesting measures, we propose linguistic aggregation operators to aggregate linguistic satisfied degrees of association rules, satisfied association rules can be selected from extracted association rules according to order on linguistic evaluation results of association rules
Summary
After Agrawal proposed association rule mining [1], it has become one of the most popular data mining techniques and contributed to many advances in the area of knowledge discovery, by which implicit, previously unknown and potentially useful knowledge can be discovered from large datasets. Association rule mining algorithms based on support and confidence measures are well-established and widely used in large datasets, such as Apriori [1], Eclat [39], FP-Growth [40] or LCM [41] algorithms, adding others interesting measures in these algorithms to mine satisfied association rules generally face memory space or response time problem. Major contributions of the paper are summarized as follows: 1) Transform selection of satisfied association rules into a decision making problem, where criteria are objective and subjective interesting measures, which are calculated by support measure, confidence measure or structure of extracted association rule, alternatives are the set of extracted association rules which are generated by existed association rule mining algorithms; 2) Present a new transformation function to transform objective and subjective interesting measures of extracted association rules into fuzzy linguistic satisfied degrees.
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