Mining of Closed Frequent Itemsets and Sequential Patterns in Data Streams Using Bit-Vector Based Method
碩士 === 國立交通大學 === 資訊科學與工程研究所 === 94 === Mining a data stream is an important data mining problem with broad applications, such as sensor network, stock analysis. It is a difficult problem because of some limitations in the data stream environment. In the first part of this paper, we propose New-Mome...
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| Format: | Others |
| Language: | en_US |
| Published: |
2006
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| Online Access: | http://ndltd.ncl.edu.tw/handle/12675863577120809139 |
| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 94 === Mining a data stream is an important data mining problem with broad applications, such as sensor network, stock analysis. It is a difficult problem because of some limitations in the data stream environment. In the first part of this paper, we propose New-Moment to mine closed frequent itemsets. New-Moment uses bit-vectors and a compact lexicographical tree to improve the performance of Moment algorithm. In the second part, we propose IncSPAM to mine sequential patterns with a new sliding window model. IncSPAM is based on SPAM and utilizes memory indexing technique to incrementally maintain sequential patterns in current sliding window. Experiments show that our approaches are efficient for mining patterns in a data stream.
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