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.
International Journal of Business Intelligence and Data Mining, 2012 Vol.7 No.4, pp.288 - 317
Available online: 27 Jan 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article