Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

Abstract

Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the

work in this paper, we have analyzed a range of widely used algorithms 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. This study

also focuses on each of the algorithm’s strengths and weaknesses for finding patterns among large item sets in

database systems

Subscribe to access this work and thousands more

Subscribe & Access