Comparing Rule Measures for Predictive Association Rules
نویسندگان
چکیده
In this paper we study the predictive ability of some association rule measures typically used to assess descriptive interest. Such measures, namely conviction, lift and χ are compared with confidence, Laplace, mutual information, cosine, Jaccard and φ-coefficient. As prediction models, we use sets of association rules generated as such. Classification is done by selecting the best rule, or by weighted voting (according to each measure). We performed an evaluation on 17 datasets with different characteristics and conclude that conviction is on average the best predictive measure to use in this setting.
منابع مشابه
Numeric Multi-Objective Rule Mining Using Simulated Annealing Algorithm
Abstract as a single objective one. Measures like support, confidence and other interestingness criteria which are used for evaluating a rule, can be thought of as different objectives of association rule mining problem. Support count is the number of records, which satisfies all the conditions that exist in the rule. This objective represents the accuracy of the rules extracted from the da...
متن کاملExtraction of Interesting Association Rules Using Genetic Algorithms
The process of discovering interesting and unexpected rules from large data sets is known as association rule mining. The typical approach is to make strong simplifying assumptions about the form of the rules, and limit the measure of rule quality to simple properties such as support or confidence. Support and confidence limit the level of interestingness of the generated rules. Comprehensibili...
متن کاملRule Evaluation Measures: A Unifying View
Numerous measures are used for performance evaluation in machine learning. In predictive knowledge discovery, the most frequently used measure is classification accuracy. With new tasks being addressed in knowledge discovery, new measures appear. In descriptive knowledge discovery, where induced rules are not primarily intended for classification, new measures used are novelty in clausal and su...
متن کاملFeature Extraction from Top Association Rules: Effect on Average Predictive Accuracy
In applications of association rule mining, it is fairly difficult to assess computationally the expected interest for the end user of each rule obtained. A number of rule quality measures have been proposed, enjoying varied properties, and leaving no clear winner. Here we propose to glean further intuition by evaluating these rule quality measures through the (possibly negative) improvement in...
متن کاملUsing Classification to Evaluate the Output of Confidence-Based Association Rule Mining
Association rule mining is a data mining technique that reveals interesting relationships in a database. Existing approaches employ different parameters to search for interesting rules. This fact and the large number of rules make it difficult to compare the output of confidence-based association rule miners. This paper explores the use of classification performance as a metric for evaluating t...
متن کامل