Sequential Pattern Mining with Multiple Minimum Supports: a Tree Based Approach
碩士 === 國立中正大學 === 資訊管理學系 === 99 === Frequent pattern mining is an important data-mining method for determining correlations among items/itemsets. Since the frequencies for various items are always varied, specifying a single minimum support cannot exactly discover interesting patterns. To solve this...
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ndltd-TW-099CCU003960032015-10-13T18:58:55Z http://ndltd.ncl.edu.tw/handle/80254266005742387182 Sequential Pattern Mining with Multiple Minimum Supports: a Tree Based Approach 考量多重門檻值之序列樣式探勘:使用樹狀結構 Yi-Chun Liao 廖一寯 碩士 國立中正大學 資訊管理學系 99 Frequent pattern mining is an important data-mining method for determining correlations among items/itemsets. Since the frequencies for various items are always varied, specifying a single minimum support cannot exactly discover interesting patterns. To solve this problem, Liu et al. propose an apriori-based method to include the concept of multiple minimum supports (MMS in short) on association rule mining. It allows user to specify MMS to reflect the different natures of items. Since the mining of sequential pattern may face the same problem, we extend the traditional definition of sequential patterns to include the concept of MMS in this study. For efficiently discovering sequential patterns with MMS, we develop a data structure, named PLMS-tree, to store all necessary information from database. After that, a pattern growth method, named PLMS-growth, is developed to discover all sequential patterns with MMS from PLMS-tree. Keywords: Sequential pattern, multiple minimum supports, pattern growth Ya-Han Hu Fan Wu 胡雅涵 吳帆 2010 學位論文 ; thesis 38 en_US |
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碩士 === 國立中正大學 === 資訊管理學系 === 99 === Frequent pattern mining is an important data-mining method for determining correlations among items/itemsets. Since the frequencies for various items are always varied, specifying a single minimum support cannot exactly discover interesting patterns. To solve this problem, Liu et al. propose an apriori-based method to include the concept of multiple minimum supports (MMS in short) on association rule mining. It allows user to specify MMS to reflect the different natures of items. Since the mining of sequential pattern may face the same problem, we extend the traditional definition of sequential patterns to include the concept of MMS in this study. For efficiently discovering sequential patterns with MMS, we develop a data structure, named PLMS-tree, to store all necessary information from database. After that, a pattern growth method, named PLMS-growth, is developed to discover all sequential patterns with MMS from PLMS-tree.
Keywords: Sequential pattern, multiple minimum supports, pattern growth
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author2 |
Ya-Han Hu |
author_facet |
Ya-Han Hu Yi-Chun Liao 廖一寯 |
author |
Yi-Chun Liao 廖一寯 |
spellingShingle |
Yi-Chun Liao 廖一寯 Sequential Pattern Mining with Multiple Minimum Supports: a Tree Based Approach |
author_sort |
Yi-Chun Liao |
title |
Sequential Pattern Mining with Multiple Minimum Supports: a Tree Based Approach |
title_short |
Sequential Pattern Mining with Multiple Minimum Supports: a Tree Based Approach |
title_full |
Sequential Pattern Mining with Multiple Minimum Supports: a Tree Based Approach |
title_fullStr |
Sequential Pattern Mining with Multiple Minimum Supports: a Tree Based Approach |
title_full_unstemmed |
Sequential Pattern Mining with Multiple Minimum Supports: a Tree Based Approach |
title_sort |
sequential pattern mining with multiple minimum supports: a tree based approach |
publishDate |
2010 |
url |
http://ndltd.ncl.edu.tw/handle/80254266005742387182 |
work_keys_str_mv |
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