Chung-Ching Yu

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The problem addressed in This work is to discover the frequently occurred sequential patterns from databases. Although much work has been devoted to this subject, to the best of our knowledge, no previous research was able to find sequential patterns from d-dimensional sequence data, where d>2. Without such a capability, many practical data would be(More)
The important issue for association rules generation is the discovery of frequent itemset in data mining. Most of the existing real time transactional databases are multidimensional in nature. The classical Apriori algorithm mainly concerned with handling single level, single-dimensional boolean association rules. These algorithms scan the transactional(More)
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