A Comparative Study of Frequent Pattern Recognization Techniques from Stream Data
|F.M.Christian1, N.C.Chauhan2, N.B.Prajapati3
|Related article at Pubmed, Scholar Google|
Mining frequent pattern from data stream is a challenging task. Finding frequent pattern from data streams have been of importance in many application such as stock market prediction, sensor data analysis, network traffic analysis, e-business and telecommunication data analysis. Frequent Pattern Stream tree  is used for maintaining frequent pattern over a period of time using modified FP tree algorithm. This approach maintains tilted time window at each node which consumes larger space. Compact Pattern Stream Tree  assumes that only current patterns are of importance and uses sliding window protocol for maintaining it. This approach does not give importance to past frequent patterns. Due to advancements in communication and storage technologies, large number of data streams has been generated by various applications and devices. Researchers have developed various methods to extract useful patterns from data streams. Many of the algorithms have been developed by extending the techniques that mines transaction data. Each methods work with different conditions such as offline streams, online streams, video streams, audio streams, etc. The performance and efficiency of the methods vary according to type of data streams. In this paper few recent and popular methods that extract patterns from stream data have been studied. Also a comparative analysis of different methods with reference to the conditions in which they work, and advantages/drawbacks of these methods are presented in this work.