Survey on Frequent Pattern Mining

Abstract

ABSRACT Mining Huge data is a problem of today’s great practical importance. However, there are some challenges for mining these huge data which includes (1) The curse of dimensionality. (2) Mining meaningful information based on the similarity measures. In this paper, we consider the technique for analyzing data, e.g., frequent pattern mining. Frequent pattern discovery finds frequently occurring events in large databases. Such data mining technique can be useful in various domains. For instance, in recommendation and e-commerce systems frequently occurring product purchase combinations are essential in user preference modeling. In the ecological domain, patterns of frequently occurring groups of species can be used to reveal insight into species interaction dynamics. Most frequent pattern mining research has concentrated on efficiency (speed) of mining algorithms. However, it has been argued within the community that while efficiency of the mining task is no longer a bottleneck, there is still an urgent need for methods that derive compact, yet high quality results with good application properties. Many organizational areas has been dedicated to research ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. Keywords: Frequent pattern, Association rule mining, Classification.