中大機構典藏-NCU Institutional Repository-提供博碩士論文、考古題、期刊論文、研究計畫等下載:Item 987654321/62992
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 78728/78728 (100%)
造访人次 : 33356777      在线人数 : 513
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.lib.ncu.edu.tw/handle/987654321/62992


    题名: 從使用者偏好資料中挖掘群體知識;Mining Group Knowledge from Users' Preference Data
    作者: 陳彥良
    贡献者: 國立中央大學資訊管理學系
    关键词: 資訊科學;軟體
    日期: 2012-12-01
    上传时间: 2014-03-17 14:16:16 (UTC+8)
    出版者: 行政院國家科學委員會
    摘要: 研究期間:10108~10207;In the last decade, the problem of getting a consensus group ranking from all users’ ranking data has received increasing attention due to its widespread applications. The group ranking problem is to construct coherent aggregated results from preference data provided by decision makers. Traditionally, the output of the group ranking problem can be classified into two types: ranking ordered lists and consensus lists. Unfortunately, these two traditional outputs suffer from their own weaknesses. For ranking ordered lists, most previous approaches pay close attention to minimize the total disagreement between multiple input rankings, to ultimately obtain an overall ranking list which represents the achieved consensus. They neglect the fact that user opinions may be discordant and have no consensus, in which we are still enforced to get a complete ranking result. For maximum consensus sequences, there may have many resulting consensus sequences which need to be checked, and thus lead to information overloading, tedious, and not easy to understand. As a result, users are difficult to grasp the whole relationship among items. To overcome these weaknesses, in this three-year project we will propose a new type of group knowledge to represent the coherent aggregated results of users’ preferences. We apply the clustering concept into the group ranking problem and generate an ordering list of segments containing a set of similarly preferred items, called consensus ordered segments, as the output. Accordingly, algorithms will be developed to discover consensus ordered segments from users’ ranking lists. In the first year, we will design a global search GA algorithm for mining consensus ordered segments. In the second year, a local search greedy algorithm with incremental capability to compute objective value will be proposed. Finally, in the last year we will propose an integrated approach, which combines the global search algorithm with the local search algorithm, to solve the consensus ordered segments mining problem. And we will apply PSO+GA to guide the global search procedure and use the greedy method + incremental operations to do the detailed search. The advantages of our approach are that (1) the knowledge is built based on users' consensuses, (2) the knowledge representation can be understood easily and intuitively, and (3) the relationships between items can be easily seen. These advantages indicate that our approach can improve the weaknesses of previous approach in mining group knowledge.
    關聯: 財團法人國家實驗研究院科技政策研究與資訊中心
    显示于类别:[資訊管理學系] 研究計畫

    文件中的档案:

    档案 描述 大小格式浏览次数
    index.html0KbHTML311检视/开启


    在NCUIR中所有的数据项都受到原著作权保护.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 隱私權政策聲明