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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4137


    Title: Mining Spatial Colocation Patterns Using Data Field Model and Fuzzy Association Rules
    Authors: Martinus
    Contributors: Department of Computer Science and Information Engineering
    Keywords: colocation pattern mining, fuzzy association rule, data field model.
    Date: 2007
    Issue Date: 2009-11-18 21:14:09 (UTC+8)
    Publisher: Asia University
    Abstract: Scientists in many researches have been using computer technologies lately. GIS, GPS have been helping scientists in doing many kinds of researches. Geographical data as a result from GPS were available in electronic format. This type of data can be treated as a spatial data. And by using colocation pattern mining, we would discover associations between spatial features. The first thing we do was developed a data set generator. Data sets that are generated by data set generator then processed using the proposed approach.
    The system we proposed was a two steps system. The first step was doing a segmentation to produce the transaction from the data set. The segmentation was using a fix threshold segmentation and the threshold was 3*sigma. Sigma in this study is our way to measure closeness of a point to its neighbors. Sigma is a distance value that will bring the entropy of the whole data set into it minimum, sigma was calculated using data field model. And the second one was doing a fuzzy association rule mining where we introduce the transaction into a fuzzy membership function. After fuzzfied the data set then we counted the fuzzy support values and fuzzy confidence values. The infrequent rules then pruned using an apriori-like algorithm. The result of the approach then come as these manners, feature a will be whether near or close or far from feature b.
    Appears in Collections:[資訊工程學系] 博碩士論文

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