Survey on Algorithms Applied in Pattern Mining

Abstract

Data mining is a collection of techniques to extract hidden and potentially useful information from large databases of various business domains. For identifying the interesting patterns and co-relation and to get benefits from the repository data, Association Rule Mining (ARM) methods are used. Pattern recognition is a major challenge within the field of data mining and knowledge discovery. In this paper, a range of widely used algorithms are analyzed for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. The paper also focuses on each of the algorithm’s strengths and weaknesses for finding patterns in different transactional dataset.