Conference Paper

Efficient Mining of Frequent Closed Itemsets without Closure Checking

DOI: 10.1109/ISDA.2008.46 Conference: Eighth International Conference on Intelligent Systems Design and Applications, ISDA 2008, 26-28 November 2008, Kaohsiung, Taiwan, 3 Volumes
Source: DBLP


Most existing algorithms for mining frequent closed itemsets have to check whether a newly generated itemset is a frequent closed itemset by using the subset checking technique. To do this, a storing structure is required to keep all known frequent itemsets and candidates. It takes additional processing time and memory space for closure checking. To remedy this problem, an efficient approach called closed itemset mining with no closure checking algorithm is proposed. We use the information recorded in a FP-tree to identify the items that will not constitute closed itemsets. Using this information, we can generate frequent closed itemsets directly. It is no longer necessary to check whether an itemset is closed or not when it is generated. We have implemented our algorithm and made many performance experiments. The results show that our approach has better performance in the runtime and memory space utilization. Moreover, this approach is also suitable for parallel mining of frequent closed itemsets.

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Available from: Jungpin Wu, Mar 27, 2015
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    • "In recent years, much attention has been paid to discovering co-occurrence patterns called closed itemsets [11], [12] from transaction databases (see Section II) in the field of data mining. Frequent pattern mining, such as the Apriori algorithm [13], searches for all frequent patterns (even if these are included in other patterns), whereas closed itemset mining can extract maximal and condensed patterns by excluding redundant patterns having inclusive relations. "
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    ABSTRACT: Automatic audio classification is a major topic in the fields of pattern recognition and data mining. This paper describes a new rule-based classification method (cREAD: classification Rule Extraction for Audio Data) for multi-class audio data. Typically, rule-based classification requires much computation cost to find rules from large datasets because of combinatorial search problem. To achieve efficient and fast extraction of classification rules, we take advantage of a closed itemset mining algorithm that can exhaustively extract non-redundant and condensed patterns from a transaction database within a reasonable time. The notable feature of this method is that the search space of classification rules can be dramatically reduced by searching for only closed itemsets constrained by “class label item”. In this paper, we show that our method is superior to the other salient methods on the classification accuracy of a real audio dataset.
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