English  |  正體中文  |  简体中文  |  Items with full text/Total items : 90429/105609 (86%)
Visitors : 10290780      Online Users : 81
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8172


    Title: Spatial data mining of colocation patterns for decision support in agriculture
    Authors: H.W. Hsiao;M.S. Tsai;S.C. Wang
    Contributors: Department of Bioinformatics
    Keywords: spatial colocation pattern;hierarchical clustering;decision support
    Date: 2006-04
    Issue Date: 2010-03-19 16:25:14 (UTC+8)
    Publisher: Asia University
    Abstract: Computer technologies have been introduced into the area of agriculture recently. Precision
    agriculture, as an example, is a popular concept of using GIS, GPS and other new technologies in
    helping farmers optimize agricultural production. Colocation pattern mining is a technique for
    discovering relationships between different thematic features in a spatial domain. For example, an
    observation that large cities are often close to riversides is obtained with a reliable statistic. Such
    desired capability is of importance in agricultural applications, like insect pest management. In this
    paper, a two-phase hierarchical clustering method is proposed to assist people in making decisions
    based on spatial colocation patterns implicitly existing inside the geographical data sets. It is designed
    to be a generic system for any data sets in point format. In the first phase, the point features being close
    together are grouped into a number of clusters. An LC matrix is generated to describe the relationship
    between the clusters and the layers of feature points. The LC matrix is then analyzed by the second
    hierarchical clustering to generate a dendrogram. The support and confidence of each single cluster in
    the dendrogram are calculated to show the concurrent occurrence of features, regardless of their
    geographical locations.
    Relation: Asian Journal of Health and Information Sciences 1(1): 61-72
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

    Files in This Item:

    File Description SizeFormat
    0KbUnknown212View/Open
    310904400-8172.doc37KbMicrosoft Word138View/Open


    All items in ASIAIR are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback