An Efficient Approach for Mining Weighted Approximate Closed Frequent Patterns Considering Noise Constraints

Abstract

Based on the frequent pattern mining, closed frequent pattern mining and weighted frequent pattern mining have been studied to reduce the search space and discover important patterns. In the previous definition of weighted closed patterns, supports of patterns are only considered to compute the closures of the patterns. It means that the closures of weighted frequent patterns cannot be perfectly checked. Moreover, the usefulness of weighted closed frequent patterns depends on the presence of frequent patterns that have supersets with the exactly same weighted support. However, from the errors such as noise, slight changes in items' supports or weights by them have significantly negative effects on the mining results, which may prevent us from obtaining exact and valid analysis results since the errors can break the original characteristics of items and patterns. In this paper, to solve the above problems, we propose a concept of robust weighted closed frequent pattern mining, and an approximate bound is defined on the basis of the concept, which can relax requirements for precise equality among patterns' weighted supports. Thereafter, we propose a weighted approximate closed frequent pattern mining algorithm which not only considers the two approaches but also suggests fault tolerant pattern mining in the noise constraints. To efficiently mine weighted approximate closed frequent patterns, we suggest pruning and subset checking methods which reduce search space. We also report extensive performance study to demonstrate the effectiveness, efficiency, memory usage, scalability, and quality of patterns in our algorithm.

A Comprehensive Survey of Frequent Itemsets Mining on Transactional Database with Weighted Items

“…In 2011, Huai et al proposed the WHIUA algorithm [22] which, following the Apriori approach and "not satisfy the downward closure property", significantly increases the search space -this is a big challenge for researchers of data mining. In the next decade, a number of algorithms were proposed such as IWFP [23], PWA [27], WAC [30], etc. but most of them still solve the problem in the direction of satisfying the "downward closure property" and the algorithm are similar to traditional frequent itemsets mining.…”

The lattice‐based approaches for mining association rules: a review

“…Traditional approaches for mining ARs consist of two steps: mining frequent itemsets (FIs)/frequent closed itemsets (FCIs)/frequent maximal itemsets (FMIs) (FIs/FCIs/FMIs), and generating rules from those itemsets. Some variants of FIs such as high utility itemsets (itemsets whose utility satisfies a given threshold), top‐ k high utility itemsets (top‐k itemsets with highest utility), weighted pattern (pattern with weighted items), erasable itemsets (itemsets can be eliminated but do not greatly affect the factory's profit), weighted erasable patterns (erasable itemsets considered the distinct weight of each item), and so on are proposed. Besides, several type of representations that limit the number of FIs such as FCIs, FMIs, top‐ k FIs, top‐rank‐ k FIs, and FIs with constraints are also proposed.…”