Abstract
Today, rapid growth in hardware technology has provided a means to generate huge volume of data continuously. Most of the real time data stream application such as network monitoring, stock market and URL filtering we found that the volume of data is so large that it may be impossible to store the data on disk. Furthermore, even if the data can be stored on the disk, the volume of the incoming data may be so large that it may be difficult to process any particular record more than once. These large volumes of data need to be mined for getting interesting patterns and relevant information out of it. Consequently, we need further enhanced technique for, data stream classification while dealing with various challenges which are not solved by traditional data mining methods such as large volume, concept drift, and concept evolution.
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