Title: FAR-miner: a fast and efficient algorithm for fuzzy association rule mining

Authors: Ashish Mangalampalli; Vikram Pudi

Addresses: International Institute of Information Technology (IIIT-H), Gachibowli, Hyderabad – 500032, India ' International Institute of Information Technology (IIIT-H), Gachibowli, Hyderabad – 500032, India

Abstract: Association rule mining (ARM) algorithms work only with binary attributes, and expect quantitative attributes to be converted to binary ones using sharp partitions, like 'age = [25, 60]'. A better alternative is to convert quantitative attributes to fuzzy attributes, like 'age = middle-aged', to eliminate loss of information due to sharp partitioning, and then run a fuzzy ARM algorithm. The most popular fuzzy ARM algorithms are fuzzy adaptations of apriori. Fuzzy apriori, like apriori, is a slow algorithm, especially for most medium-sized (500 K to 1 M) and large ( > 1 M) datasets. We propose a new fuzzy ARM algorithm called FAR-miner for fast and efficient performance. Through experiments we show that FAR-miner is 8-19 and 6-10 times faster on large and medium-sized datasets respectively as compared to fuzzy apriori. This efficiency is due to properties like two-phased multiple-partition tidlist-style processing and byte-vector representation and effective compression of tidlists.

Keywords: fuzzy logic; association rule mining; fuzzy ARM; fuzzy partitioning; fuzzy relations; partitions; tidlist compression; fuzzy rule mining.

DOI: 10.1504/IJBIDM.2012.051714

International Journal of Business Intelligence and Data Mining, 2012 Vol.7 No.4, pp.288 - 317

Received: 29 Jun 2012
Accepted: 29 Sep 2012
Published online: 12 Nov 2014 *

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