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
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th 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
Approximate counts and quantiles over sliding windows
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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Sequential pattern mining is an important problem in many data mining applications. When the data comes as stream, its mining becomes difficult. Unlike relational database the stream data size keep growing while the memory size is fixed relatively. So it is unfeasible to store all the past data in many applications. Hence one-time scan algorithm is needed to execute mining on stream data. As well as the increasing application of sensors, data type of image, video are produced in a rapid speed. Those data usually are with high dimensionality. Inspired by this, we present a new method of mining high dimensional stream data in this paper. A model based on forest structure is developed to index the sequence and to find the new patterns. The algorithm is evaluated on a set of large synthetic data stream.