In the field of Data Mining (DM), Frequent Pattern Mining (FPM) is an active research field because the FP in data are used to gather knowledge which may help in decision making. In addition, FPM has various applications in many domains such as software bug detection, biological and spatiotemporal data. In short duration, the aspects of FPM have been explored very widely due to its algorithmic nature. Various literature works made a tremendous progress in this research area yielding effective techniques to mine the data in transaction databases, ranging from scalable and efficient algorithms for FPM to numerous research frontiers like correlation mining, clustering, structural and sequential pattern mining, associative classification, as well as their broad applications. The main objective of this work is to survey the current status of FPM and its research directions. The scope of data analysis were broadened substantially by FPM and also had an impact on DMApplications(DMA) and Techniques(DMT). But still, there are many research challenges need to be solved before FPM can claim as a basic approach in DMA.
Volume 11 | 04-Special Issue
Pages: 1725-1730