2008 QuantitativeEvaluationofApproxi

(Gupta et al., 2008) ⇒ Rohit Gupta, Gang Fang, Blayne Field, Michael Steinbach, and Vipin Kumar. (2008). “Quantitative Evaluation of Approximate Frequent Pattern Mining Algorithms.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401930

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Abstract

Traditional association mining algorithms use a strict definition of support that requires every item in a frequent itemset to occur in each supporting transaction. In real-life datasets, this limits the recovery of frequent itemset patterns as they are fragmented due to random noise and other errors in the data. Hence, a number of methods have been proposed recently to discover approximate frequent itemsets in the presence of noise. These algorithms use a relaxed definition of support and additional parameters, such as row and column error thresholds to allow some degree of "error" in the discovered patterns. Though these algorithms have been shown to be successful in finding the approximate frequent itemsets, a systematic and quantitative approach to evaluate them has been lacking. In this paper, we propose a comprehensive evaluation framework to compare different approximate frequent pattern mining algorithms. The key idea is to select the optimal parameters for each algorithm on a given dataset and use the itemsets generated with these optimal parameters in order to compare different algorithms. We also propose simple variations of some of the existing algorithms by introducing an additional post-processing step. Subsequently, we have applied our proposed evaluation framework to a wide variety of synthetic datasets with varying amounts of noise and a real dataset to compare existing and our proposed variations of the approximate pattern mining algorithms. Source code and the datasets used in this study are made publicly available.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 QuantitativeEvaluationofApproxi	Vipin Kumar Michael Steinbach Rohit Gupta Gang Fang Blayne Field			Quantitative Evaluation of Approximate Frequent Pattern Mining Algorithms				10.1145/1401890.1401930