FREQUENT CORRELATED PERIODIC PATTERN MINING FOR LARGE VOLUME SET USING TIME SERIES DATA

Abstract

Frequent pattern mining has been a widely used in the area of discovering association and correlations among real data sets. However, discovering interesting correlation relationship among huge number of co-occurrence patterns are complicated, a majority of them are superfluous or uninformative. Mining correlations among large pile of useless information is extraordinarily useful in real-time applications. In this study, we propose a technique uses FP-tree for mining frequent correlated in periodic patterns from a transactional database. The analysis of time correlation measure tend to improvise the performance based on real time data sets and the result proves the algorithm efficiency by shifting the data sets to various domain towards time series, its correlation and noise-resilient ratio. This work addresses the time correlation factor achieved with the previous evaluated result of time series sequence of FP tree.