Name
Abstract | ||
|---|---|---|
Recent emerging applications, such as network traffic analysis, web click stream mining, power consumption measurement, sensor network data analysis, and dynamic tracing of stock fluctuation, call for study of a new kind of data, stream data. Many data stream management systems, prototype systems and software components have been developed to manage the streams or extract knowledge from stream data. Mining frequent patterns is a foundational job for the methods of data mining and knowledge discovery. This paper proposes an algorithm for mining the recent frequent patterns over an online data stream. This method uses RFP-tree to store compactly the recent frequent patterns of a stream. The content of each transaction is incrementally updated into the pattern tree upon its arrival by scanning the stream only once. Moreover, the strategy of conservative computation and time decaying model are used to ensure the correctness of the mining results. Finally, the performance results of extensive simulation show that our work can reduce the average processing time of stream data element and it is superior to other analogous algorithms. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1142/S0218194009004325 | INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING |
Keywords | Field | DocType |
Data stream, frequent pattern mining, sliding window | Data mining,Traffic analysis,Data stream mining,Data stream clustering,Clickstream,Data stream,Computer science,Knowledge extraction,Transaction data,Tracing,Database | Journal |
Volume | Issue | ISSN |
19 | 5 | 0218-1940 |
Citations | PageRank | References |
1 | 0.36 | 5 |
Authors | ||
1 | ||