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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/16006

Title: Applying AI Technology and Rough Set Theory for Mining Association Rules to Support Crime Management and Fire-fighting Resources Allocation
Authors: Lee, Show-Chin;Huang, Mu-Jung
Contributors: 資訊管理學系
Keywords: 犯罪管理;火災搶救
Data mining;Self-organizing map;SOM;Rough set theory;Rule-based knowledge;Crime management
Date: 2002-12
Issue Date: 2013-04-22T07:39:10Z
Publisher: 中央警察大學資訊管理研究所
Abstract: The missions for the police and fire fighters are to protect for public safety and to fight and prevent from fires, respectively. In this dynamic environment, however, there are many potential dangers and uncertain factors that can’t be predicted. In order to improve the total performance, some rules extracted from criminal and fire-fighting records are needed. The purpose of this paper is to mine association rules from a database to support crime management or fire-fighting resources allocation. The mining procedure consists of two essential modules. One is a clustering module based on a neural network, a Self-Organization Map (SOM), which performs grouping tasks on the tremendous number of database records. The another is a rule extraction module applying rough set theory that can extract association rules for each homogeneous cluster and the relationships between different clusters. An example is for illustration.
Relation: 資訊、科技與社會學報, 2(2): 65-77
Appears in Collections:[資訊管理學系所] 期刊論文

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