Comparative analysis of sequence weighting approaches for mining time-interval weighted sequential patterns

Chang, Joong Hyuk; Park, Nam Hun

doi:10.1016/j.eswa.2011.09.100

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…One is the mining algorithm based on item interval constraint, the other is extended sequence mining algorithm. Mining algorithm based on item interval constraints is to extract sequential patterns which would satisfy the minimum weighted support and item interval constraint that the user specified [2,3]. Extended sequence mining algorithm in order to extract sequence patterns that contains the same item but different item interval constraint by to perform algorithm just one time, puts forward the algorithm based on extended sequence [4][5][6][7], these algorithms by converting item interval into pseudo item to extract contains item interval sequence mode.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Weighted Closed Sequential Pattern Mining Algorithm with Item Interval

Lü¹,

Li²

2015

Advances in Intelligent Systems Research

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Abstract. The algorithm of this paper inserts pseudo items which are converted from item interval to obtain equal extended sequence database; it defines item-interval constraints, which are relative to the item weight, to prune the mining patterns. Through doing this, the algorithm avoids mining the patterns which users are not interested in and shortens the running time. It adopts histogram statistic pattern to get the standardization description to item interval of the mining patterns, making the mining sequences include the item interval information which is valuable to user decision.

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