Survey on Constrained based Data Stream Mining

Abstract

In most of the real time applications data may arrive as continuous ordered sequence of items, called Data streams. The main challenge in dealing with the Data Stream is its 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 analyze 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 user. In order to obtain that, some constraint based mining techniques, which acts as a filter to the large result set retrieved from traditional pattern mining techniques. This paper investigates different data stream mining techniques and constrained based stream mining techniques from which the user gets the required information from the data stream. General Terms Data Stream, Pattern.