Generation of Frequent Patterns with Weights Over Continuous Flow of Data Efficiently

Provided by: International Journal of Soft Computing and Engineering (IJSCE)
Topic: Data Management
Format: PDF
Mining data streams for knowledge discovery has been used in many applications like web click stream mining, network traffic monitoring, network intrusion detection, and dynamic tracing of financial transactions. In this paper, by analyzing characteristics of date stream, the authors propose an efficient algorithm Weighted Frequent Pattern (WFP) mining that discovers more knowledge compared to traditional frequent pattern mining. The existing algorithms cannot apply for stream of data because those algorithms require multiple database scans. This technique uses a single database scan for mining stream of data.

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