Mining Sequential Patterns in Dense Databases

Gouda, Karam; Hassaan, Mosab

doi:10.5121/ijdms.2011.3112

Cited by 6 publications

(2 citation statements)

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“…In [7], Gouda and Hassaan argue that typical sequential pattern mining algorithms tend to lose their efficiency when applied to a dense database. Their experiments confirm that the execution time increases exponentially as the number of frequent sequences increases even when the execution times in their experiments remain in the order of a few hundred seconds.…”

Section: Performance Issuesmentioning

confidence: 99%

Finding sequential patterns with TF-IDF metrics in health-care databases

Kardkovács

Kovács

2014

Acta Universitatis Sapientiae, Informatica

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Finding frequent sequential patterns has been defined as finding ordered list of items that occur more times in a database than a user defined threshold. For big and dense databases that contain really long sequences and large itemset such as medical case histories, algorithm proposed on this idea of counting the occurrences output enourmous number of highly redundant frequent sequences, and are therefore simply impractical. Therefore, there is a need for algorithm that perform frequent pattern search and prefiltering simultaneously. In this paper, we propose an algorithm that reinterprets the term support on text mining basis. Experiments show that our method not only eliminates redundancy among the output sequences, but it scales much better with huge input data sizes. We apply our algorithm for mining medical databases: what diagnoses are likely to lead to a certain future health condition.

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Section: Performance Issuesmentioning

confidence: 99%

Finding sequential patterns with TF-IDF metrics in health-care databases

Kardkovács

Kovács

2014

Acta Universitatis Sapientiae, Informatica

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“…The vertical data format can be obtained by transforming from a horizontally formatted sequence database in just one scan; however the basic methodology is breadth-first search and Apriori pruning [9]. Despite the pruning SPADE have to generate large sets of candidates in breadth-first manner in order to grow longer sequences [10][11][12]. However, to convert horizontal format to vertical format, requires high computational cost, which includes additional processing time and additional storage space, multiple times larger than the original sequence database [13][14][15].…”

Section: Literature Reviewmentioning

confidence: 99%