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Frequent itemset mining is a classic problem in data mining. However, most algorithms have to scan databases many times. This paper presents an algorithm that can find maximal frequent itemsets quickly. In this algorithm, each transaction is represented as a binary vector, so the task of discovering maximal frequent itemsets is turn to search frequent patterns in binary vector set. The algorithm is...
The time-varying databases, whose data distribution are changed with time. This database is frequently observed in many application areas including manufacturing, financing, and marketing. Knowledge discovery in time-varying databases is an important subject of data mining technology. This paper presents a moving-window neural network classification algorithm that can effectively classify the time-varying...
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