A STUDY ON CONSTRAINED FREQUENT PATTERN MINING OF STREAMING DATA

Dr.B. Lavanya

Abstract


: In most of the real time applications data may arrive as continuous ordered sequence of items, called data streams. Constraints are used to specify rules for the data. This study deals with different data stream mining and sequence pattern mining techniques and found that most of the techniques recover a most number of results. Data stream is voluminous, complex and dynamically arriving stream of data. There are certain techniques to deal with data streams, in particular, finding the frequent or sequential patterns that occur repeatedly. These results retrieve huge number of patterns, which are hard to analyse and use them, also difficult to store these results and its intermediate results. The traditional pattern mining techniques fail to give the relevant details to the use, which is hard and get the material information for the user in order to filter results/patterns receive from those techniques, constraint based mining approach, in which user can give the constraints and get the required and right information from the data stream.

 

 Keywords: Data stream mining, Frequent mining, Constraint, Sequential data mining, pattern mining.


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