Authors: Negar Gh Ghanbari; Mohammad Reza Gholamian
Addresses: School of Industrial Engineering, Iran University of Science and Technology, P.O. Box 1684613114, Narmak, Tehran, Iran. ' School of Industrial Engineering, Iran University of Science and Technology, P.O. Box 1684613114, Narmak, Tehran, Iran
Abstract: In this paper, we consider the problem of finding sequential patterns in multi-levels, with aid of candidate generate and test approach. Based on this technique, many sequential pattern algorithms have been developed, but few of them have paid attention to multi-level concept. We thus purpose a new efficient algorithm, called dynamic vertices levelwise (DVlw) for mining multi-level sequence patterns. It uses the same principals as other candidate generation and test algorithms but handles multi-levelled property for sequences prior to and separately from the testing and counting steps of candidate sequences. Empirical evaluation using synthetic data indicates that the proposed algorithm performs significantly faster than a state-of-the-art algorithm with this approach.
Keywords: knowledge extraction; knowledge discovery; sequential patterns; pattern mining; multi-level patterns; data mining.
International Journal of Business and Systems Research, 2012 Vol.6 No.3, pp.269 - 278
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 15 Jul 2012 *