FAR-miner: a fast and efficient algorithm for fuzzy association rule mining
Ashish Mangalampalli, Vikram Pudi · International Journal of Business Intelligence and Data Mining · 2012
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.