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Mining Top-K of Stream Frequent Patterns



Mining of sequential pattern algorithms are the most important in data mining field and are the key of many knowledge discovery applications. However, running such applications need memory and time, particularly when dealing with vast amounts databases. Choosing the unsuitable support threshold is the main factors to consume additional memory as well as time. On the other hand, it may present huge numerous of frequent patterns and that is hard to obtain the useful patterns, and it is not easy to compare the results. The problem itself will be increased and be more complicated, especially If the sequences are long such as stream sequences. To solve this problem we proposed a novel Top-K algorithm, that uses its patterns output to dynamically set and update the best minimum support, where K is the most essential and appropriate frequencies (with the best support). However, to avoid consuming much time and memory we proposed the algorithm based on pseudo-projection and BI-Directional Extension collectively with search space pruning functions. The extensive study and experiments were done on various real clickstream datasets and demonstrate our algorithm is more efficient compared with the related algorithms.



Total Pages: 11


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Online Article


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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