Mining Sequential Patterns in Data Stream

Authors:
Qinhua Huang;Weimin Ouyang
Affiliations:
Modern Education Technique Center, Shanghai University of Political Science and Law, Shanghai, China 201701;Modern Education Technique Center, Shanghai University of Political Science and Law, Shanghai, China 201701
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

Citing 11
Cited 0

Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
FreeSpan: frequent pattern-projected sequential pattern mining

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences

Machine Learning
A tree projection algorithm for generation of frequent item sets

Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Mining Sequential Patterns: Generalizations and Performance Improvements

EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Mining Sequential Patterns

ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
The PSP Approach for Mining Sequential Patterns

PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
A simple algorithm for finding frequent elements in streams and bags

ACM Transactions on Database Systems (TODS)
Sequential PAttern mining using a bitmap representation

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth

ICDE '01 Proceedings of the 17th International Conference on Data Engineering
Approximate counts and quantiles over sliding windows

PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a new algorithm of mining sequential patterns in data stream. In recent years data stream emerges as a new data type in many applications. When processing data stream, the memory is fixed, new stream elements flow continuously. The stream data can not be paused or completely stored. We develop a LSP-tree data structure to store the discovered sequential patterns. The experiment result shows that our proposal is able to mine sequential patterns from stream data with rather low price.