fi-fg: frequent item sets mining from datasets with high number of transactions by granular computing and fuzzy set theory

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ID: 201735
2015
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Abstract
Mining frequent item set (FI) is an important issue in data mining. Considering the limitations of those exact algorithms and sampling methods, a novel FI mining algorithm based on granular computing and fuzzy set theory (FI-GF) is proposed, which mines those datasets with high number of transactions more efficiently. Firstly, the granularity is applied, which compresses the transactions to some granules for reducing the scanning cost. During the granularity, each granule is represented by a fuzzy set, and the transaction scale represented by a granule is optimized. Then, fuzzy set theory is used to compute the supports of item sets based on those granules, which faces the uncertainty brought by the granularity and ensures the accuracy of the final results. Finally, Apriori is applied to get the FIs based on those granules and the new computing way of supports. Through five datasets, FI-GF is compared with the original Apriori to prove its reliability and efficiency and is compared with a representative progressive sampling way, RC-SS, to prove the advantage of the granularity to the sampling method. Results show that FI-GF not only successfully saves the time cost by scanning transactions but also has the high reliability. Meanwhile, the granularity has advantages to those progressive sampling methods.
Reference Key
zhang2015mathematicalfi-fg: Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Zhong-jie Zhang;Jian Huang;Ying Wei
Journal journal of power sources
Year 2015
DOI
10.1155/2015/623240
URL
http://dx.doi.org/10.1155/2015/623240
Keywords
civil engineering (general)mathematics engineering (general)

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