Performance Evaluation of Approximate Pattern Mining Based on Probabilistic and Statistical Techniques

Abstract

Approximate frequent pattern mining is to find approximate patterns, not exact frequent patterns with tolerable variations for more efficiency. As the size of database increases, much faster mining techniques are needed to deal with huge databases. Moreover, it is more difficult to discover exact results of mining patterns due to inherent noise or data diversity. In these cases, by mining approximate frequent patterns, more efficient mining can be performed in terms of runtime, memory usage and scalability. In this paper, we benchmark efficient algorithms of mining approximate frequent patterns based on statistical and probabilistic methods. We study the characteristics of approximate mining algorithms, and perform performance evaluations of the state of the art approximate mining algorithms. Finally, we analyze the test results for more improvement.