資料分群（Data Clustering）常被視為在做資料挖掘（Data Mining）時的一個初步動作，特別是在具有大量及高維度的資料集合中。資料經由良好的分群，將可發掘原本隱藏於資料中之有用資訊，以後續用來幫助企業做問題解決與決策制訂。其中切割式分群演算法在搜尋分群最佳解的作法上，當面對龐大的資料量時，常需要耗費大量的時間成本，且無法自動產生適合的分群數目，必須由使用者於事前給定，而這通常是最困難的部分。同時，當資料的一些基礎屬性描述空間（Description Spaces）無法充分表示該維度的複雜性時，則此演算法可能也會得到不良的分群結果。因此，本研究將根據以上問題，以切割式演算法中之PAM演算法為主，提出合併解決方案。針對PAM分群演算法，結合啟發式演算法與概念階層（Concept Hierarchy）中屬性層級爬升之觀念，以在分群過程中找到適合的群聚數目，並改善該演算法需要大量時間成本於最佳解搜尋上之缺點，最後使分群結果更具有意義及品質更好。

　　Usually, data clustering is used to be a preliminary step in data mining, especially in the mass and multiple dimensions dataset. After appropriate clustering, useful information can be found in the hidden data. This information can support the enterprise to do problem-solving and decision-making. When the data is mass, using partition clustering algorithm in searching optimal clustering often take a lot of time and cannot generate the appropriate cluster number. The partition clustering algorithm need user to set the initial cluster number which is usually the most difficult part in clustering. Furthermore, when the data description spaces cannot describe the complexity of the data dimensions sufficiently, the algorithm may result in a poor clustering. According to the above description, this research proposes a solution based on PAM algorithm. By combining the heuristic algorithm and the concept of attribute level climbing, the algorithm can decrease the spending time of searching optimal solution and find the appropriate cluster number. Finally, it leads the clustering result more comprehensible and better.

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國家衛生研究院（民92），「全民健康保險研究資料庫—學術研究類專用譯碼簿」，國家衛生研究院，頁1-15-1-17，2-67-2-68。

李世代（民89），「全民健康保險慢性疾病建議代碼」，中央健康保險局委託計畫。

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全民健康保險研究資料庫網站，http://www.nhri.org.tw/nhird/