Secure and unifold mining model for pattern discovery from streaming data

Abstract

The intimidating challenge is practice of data mining (DM) over the streams of data because of its continuous data streaming. On the data streams, the practices of mining should be performed on cluster of streamed records in specified interval of time. The representation of window is the buffered records set which might be dynamic or static in the size. When compared with other practices of mining, the 'frequent pattern mining' on the streams of data are crucial. This occurs because, for predicting the pattern frequency, many of the existing methods repeatedly scan entire buffered transactions. This denotes the intricacy of procedure and overhead of memory. This paper proposes novel DM algorithms in particular for identifying the frequent patterns from indefinite data streams which scans every window once, therefore windows buffered records is pruned that evades computational and memory overhead. 'Unifold mining model for pattern discovery from streaming data' is the contribution of this paper. The outperformance of UMM when compared with other contemporary models is represented by crucial assessment of algorithm and optimisation schemes.