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To the best of our knowledge, the problem of mining multi-relational frequent patterns in data streams is still unsolved up to now. To attack this problem, an algorithm RFPS, which is based on novel data synopsis and declarative bias, is proposed in this paper. By introducing a new data synopsis method, where period sampling is used, many samplespsila checking operations are avoided. Meanwhile, lots...
Mining frequent itemsets in data streams has became one of the hottest research topics in data mining nowadays, recent algorithms that make use of definite error bound or probabilistic error bound, have relieved the temporal-spatial complexity at some extent. However, the introduction of unwanted sub-frequent itemsets, and the changes of itemsetspsila supports, namely concept drifts, lower the efficiency...
How to reduce the number of frequent itemsets effectively is a hot topic in data mining research. Clustering frequent itemsets is one solution to the problem. Since generators are lossless concise representations of all frequent itemsets, clustering generators is equivalent to clustering all frequent itemsets. This paper proposes a new algorithm for clustering frequent itemsets based on generators...
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