Frequent pattern mining is an important data mining problem with broad applications. Although there are many in-depth studies on efficient frequent pattern mining algorithms and constraint pushing techniques, the effectiveness of frequent pattern mining remains a serious concern: it is non-trivial and often tricky to specify appropriate support thresholds and proper constraints. In this paper, ...

The mining of frequent patterns (or frequent itemsets) plays an essential role in many tasks of data mining. One major methodology for mining frequent patterns is the Apriori-based approach, which is computationally costly because many candidate itemsets have to be generated and verified. More recently, another approach using the Frequent-Pattern Tree (FP-tree) have been suggested to avoid the ...

The mining of the complete set of frequent itemsets will lead to a huge number of itemsets. Fortunately, this problem can be reduced to the mining of frequent closed itemsets (FCIs), which results in a much smaller number of itemsets. The approaches to mining frequent closed itemsets can be categorized into two groups: those with candidate generation and those without. In this paper, we propose...

Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional itemset mining. This is because the AFI algorithms generate large number of candidate itemsets. article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution proposed approach it mines core patterns and examines approximate conditions directly wi...