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A priori algorithm is a classical algorithm of association rule mining and also is one of the most important algorithms. But it also has some limitations. It produces overfull candidates of frequent itemsets, so the algorithm needs scan database frequently when finding frequent itemsets. So it must be inefficient. To solve the bottleneck of the a priori algorithm, this paper introduces an improved...
Sequential pattern mining has become more and more popular in recent years due to its wide applications and the fact that it can find more information than association rules. Two famous algorithms in sequential pattern mining are AprioriAll and PrefixSpan. These two algorithms not only need to scan a database or projected-databases many times, but also require setting a minimal support threshold to...
The discovery of association rules in data mining is an important issue, the core of which is the frequent pattern mining, Apriori algorithm is classical for the association rule mining, but it should repeatedly scan the database and can produce plenty of candidates. By examples, it is proved that Boolean matrix association rules algorithm can improve the algorithmic efficiency by reducing the times...
In order to overcome the drawbacks of apriori algorithm for mining frequent itemsets, TIMV (Three-dimensional Itemsets Matrix and Vectors) algorithm was proposed, which used three -dimensional itemsets matrix and vectors, and broke through the bottom-up framework of Apriori. Only needed one pass to scan the database and did not create candidate itemsets, we could gain all the frequent itemsets. Furthermore,...
In association rules, although Apriori algorithm uses cut-technology when it generates item sets of candidates, it has to scan the entire database while scanning the transaction database each time. The scanning speed is very slow for its large amount of data. The improved Apriori algorithm based on matrix is improved from the Apriori algorithm and the matrix algorithm. Its basic idea is transforming...
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