A COMPARATIVE LEARNING ON REGULAR PATTERN MINING ALGORITHMS

Abstract

Frequent pattern mining has been an important subject matter in data mining from many years. Many efficient algorithms have been designed for finding frequent search patterns in transactional database .Discovering frequent itemsets is the computationally intensive step in the task of mining association rules. A large number of candidate itemsets generation is one of the main challenge in mining. The objective of frequent pattern mining is to find frequently appearing subsets in a given sequence of sets. Frequent pattern mining comes across as a sub-problem in various other fields of data mining such as association rules discovery, classification, market analysis, clustering, web mining, etc. Various methods and algorithms have been proposed for mining frequent pattern.This paper presents comparative study on frequent mining techniques – Apriori and FP-Growth.