叢集分析為資料探勘問題的技術之一，在目前的網路環境快速發展下，資料屬性的數目與種類也大量增加，導致傳統分群技術執行的效能大幅降低。因為分割式分群的執行效率相較於其他分群法為高且最為簡單如k-means，因此相關方面的研究也最多，不過由於資料屬性種類繁多，大多研究都只針對數值與類別屬性同時存在的情況，或是單純對順序屬性著墨，但往往資料分群的結果不是正確性不佳，就是分群動作所需耗費的時間成本太高，在資料分群效能以及效率上仍有待加強。本研究架構以k-means為基礎，針對三種屬性同時存在的資料集做分析，並以各自不同的相似度定義作為分群考量，類別屬性以共同發生值為距離考量，以及順序屬性以媒合度的觀點來計算之，並以群內距離最小，群間差異最大的觀念來訂定分群的衡量指標，把三種屬性各視為一個區塊，對群心做各種排列組合，將所有組合都做分群動作，最後將衡量指標最好的群心組合當做下一次分群的初始群心，直到衡量指標變動量小於限定值或不變化則停止。並且提出相對應的基因演算法，本研究考慮三種屬性同時存在的資料進行分群，將順序屬性從數值屬性獨立出來，並由此得知將基因演算法的確對叢集分析效能有所幫助。

Clustering is one of the most important technology in data mining. With the fast development of networks, large numbers of data attributes and items reduce the efficiency of data processing substantially. Among various clustering technologies, partitional clustering is easier to implement and can be employed faster than other methods of clustering. However, different types of data attributes can make clustering complicated. Most of literature concentrates on numerical and categorical, or only ordinal attributes. But the results seem not very well. In terms of accuracy or execution time. The proposed model, based on k-means, has a major objective to deal with the three attributes’ numeric, categorical and ordinal simultaneously. Numerical similarity is counted with Euclidean distance, categorical similarity is derived by the times of each value’s rank, and ordinal similarity is defined by the match degree. The effectiveness of the proposed approach is evaluated by the use of essential concept of clustering which is to minimum the ratio of the within cluster errors to the between cluster errors. A genetic algorithm is also proposed for reducing the execution time, to deal with the three types of attributes at the same time.

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