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An Efficient Top-Down Approach for Mining Frequent Patterns
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In this thesis, we propose two important methods to deal with the problems of mining hybrid sequential patterns and mining frequent itemsets. Hybrid sequential patterns and frequent itemsets are two mining techniques which provide useful information for the improvement of the analysis of marketing strategies. High-performance computing is crucial for these two techniques in order to ensure system scalability when the size and complexity of the database increase. We first propose a hierarchical mining technique to deal with the performance issue. The unique features of this new technique include the following: counting hybrid sequential patterns by class and examining database transactions in a top-down manner. Further, in order to improve the space problem in the transaction decomposition method, we propose another algorithm that combines the transaction decomposition method and the clustering technique. The improved algorithm separates the lattice into small pieces so that each portion can be solved by the decomposition method independently. Experimental results show that this improved algorithm has a fast execution time with respect to the mining of a transaction database. In summary, this thesis makes four major contributions to the field of data mining. First, a new method to count a group of patterns simultaneously is provided by the proposed pattern-class concept. Second, a novel decomposition model to lower the I/O cost for counting the patterns from a large database is proposed. Third, the correctness of counting the patterns in the decomposition method is proved. Lastly, an algorithm for improving the decomposition method is introduced, and the performance comparison of the proposed algorithm with the existing algorithms is presented in this thesis.
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