Mining Regular Pattern Over Dynamic Data Stream using Bit Stream Sequence
Vijay Kumar Verma1, Kanak Saxena2

1Vijay Kumar Verma, Department of Computer Science and Engineering, Lord Krishna College of Technology, Indore (M.P), India.
2Dr. Kanak Saxena, Professor and Head, Department of Computer Application, Samrat Ashok Technological, Institute Vidisha Indore (M.P), India.
Manuscript received on 8 December 2013 | Revised Manuscript received on 18 December 2013 | Manuscript Published on 30 December 2013 | PP: 7-10 | Volume-3 Issue-7, December 2013 | Retrieval Number: E1256103513/13©BEIESP
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Abstract: In recent years, data streams have become an increasingly important area of research for the computer science, database and data mining communities. Data streams are ordered and potentially unbounded sequences of data points created by a typically nonstationary generation process. Common data mining tasks associated with data streams include clustering, classification and frequent pattern mining[1]. Recently, temporal regularity in occurrence behavior of a pattern was treated as an emerging area in several applications A pattern is said to be regular in a data stream, if its occurrence behavior is not more than the user given regularity threshold. Although there has been some efforts done in finding regular patterns over stream data. In this paper we develop a new method called Mining regular Pattern using Bit Stream Sequence with sliding window to generate the complete set of regular pattern over a data stream at a user given regularity threshold. Experimental results show that highly efficiency in terms of execution and memory consumption and also in number of candidate scan.
Keywords: Data Mining, Data Stream, Pattern Mining, Regular Pattern, Temporal Regularity, Sliding Window.

Scope of the Article: Pattern Recognition