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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Hong, TPSubject: search.filters.subject.constraint-based mining

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Now showing 1 - 3 of 3
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    A three-phased online association rule mining approach for diverse mining requests
    (2004)
    Wang, CY
    ;
    Tseng, SS
    ;
    Hong, TP
    ;
    Chu, YS
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    資訊工程學系
    In the past, most incremental mining and online mining algorithms considered finding the set of association rules or patterns consistent with the entire set of data inserted so far. Users can not easily obtain the results from their only interested portion of data. For providing ad-hoc, query-driven and online mining supports, we first propose a relation called multidimensional pattern relation to structurally and systematically store the context information and the mining information for later analysis. Each tuple in the relation comes from an inserted dataset in the database. This concept is similar to the construction of a data warehouse for OLAP. However, unlike the summarized information of fact attributes in a data warehouse, the mined patterns in the multidimensional pattern relation can not be directly aggregated to satisfy, users' mining requests. We then develop an online mining approach called Three-phased Online Association Rule Mining (TOARM) based on the proposed multidimensional pattern relation to support online generation of association rules under multidimensional considerations. Experiments for both homogeneous and heterogeneous datasets are made, with results showing the effectiveness of the proposed approach.
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    Item type:Item,
    Multidimensional on-line mining
    (2006)
    Wang, CY
    ;
    Hong, TP
    ;
    Tseng, SS
    ;
    資訊工程學系
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    Department of Computer Science
    In the past;, incremental mining approaches usually considered getting the newest; set; of knowledge consistent; with the entire set of data inserted so far. Users can not, however, use them to obtain rules or patterns only from their interesting portion of the data. In addition, these approaches only focused on finding frequent patterns in a specified part of a database. That is, although the data records are collected in under certain time, place and category, such contexts (circumstances) have been ignored in conventional mining algorithms. It will cause the lack of patterns or rules to help users solve problems at, different, aspects and with diverse considerations. In this paper, we thus attempt; to extend incremental mining to online decision support under multidimensional context considerations. We first propose the multidimensional pattern relation to structurally and systematically retain the additional context information and mining information for each inserted dataset into a database. We then develop an algorithm based on the proposed multidimensional pattern relation to correctly and efficiently fulfill diverse on-fine mining requests.
  • Thumbnail Image
    Item type:Item,
    Flexible online association rule mining based on multidimensional pattern relations
    (2006-06-22)
    Wang, CY
    ;
    Tseng, SS
    ;
    Hong, TP
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide ad-hoc, query-driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis. Each tuple in the relation comes from an inserted dataset in the database. We then develop an online mining approach called three-phase online association rule mining (TOARM) based on this proposed multidimensional pattern relation to support online generation of association rules under multidimensional considerations. The TOARM approach consists of three phases during which final sets of patterns satisfying various mining requests are found. It first selects and integrates related mining information in the multidimensional pattern relation, and then if necessary, re-processes itemsets without sufficient information against the underlying datasets. Some implementation considerations for the algorithm are also stated in detail. Experiments on homogeneous and heterogeneous datasets were made and the results show the effectiveness of the proposed approach. (c) 2005 Elsevier Inc. All rights reserved.