Objectively evaluating condensed representations and interestingness measures for frequent itemset mining
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- 作者：Albrecht Zimmermann
- 关键词：Result verification ; Data generation ; Interestingness measures
- 刊名：Journal of Intelligent Information Systems
- 出版年：2015
- 出版时间：December 2015
- 年：2015
- 卷：45
- 期：3
- 页码：299-317
- 全文大小：779 KB
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1. KU Leuven, Celestijnenlaan 200A, 3001, Leuven, Belgium - 刊物类别：Computer Science
- 刊物主题：Data Structures, Cryptology and Information Theory
Artificial Intelligence and Robotics
Document Preparation and Text Processing
Business Information Systems - 出版者：Springer Netherlands
- ISSN：1573-7675
文摘
Itemset mining approaches, while having been studied for more than 15 years, have been evaluated only on a handful of data sets. In particular, they have never been evaluated on data sets for which the ground truth was known. Thus, it is currently unknown whether itemset mining techniques actually recover underlying patterns. Since the weakness of the algorithmically attractive support/confidence framework became apparent early on, a number of interestingness measures have been proposed. Their utility, however, has not been evaluated, except for attempts to establish congruence with expert opinions. Using an extension of the Quest generator proposed in the original itemset mining paper, we propose to evaluate these measures objectively for the first time, showing how many non-relevant patterns slip through the cracks. Keywords Result verification Data generation Interestingness measures