資料分群(clustering)與資料分類法(classification)分屬資料探勘方法中的非監督式（unsupervised）及監督式(supervised)學習方法。當資料的標籤已知時，分類法可用來訓練一個分類模型將未知標籤的資料進行分類。若資料的標籤無法事先得知，則分群方法可用來解析資料中潛在的結構。本研究的目標在發展一個整合資料分群及資料分類技術的架構並運用多準則決策模型(non-dominated sorting generic algorithm for combining clustering and classification, NSGA-CCC)來有系統地分析複雜資料。結合資料分群及資料分類的特性，並將之施行於資料集中不同的資料型態：效能資料 (Q 資料集) 及與效能有關的資料 (X資料集)來進行資料分析。資料分群方法首先施行於Q資料集，將量化資料進行分群。其分群的結果（即資料的分群標示）再結合X資料集以作為資料分類方法的施行對象。在本研究中，基於樹狀結構的可讀性及成果的穩定性，階層分群法與決策樹方法作為資料分群及分類方法的例子。結合多準則決策分析non-dominated sorting generic algorithm-II方法，來同時檢視資料分群及分類方法的成效以選擇適合的群組數以平衡兩方法的效能。本研究以零售銷售業的銷售時點資料集作為此一方法的應用例子，來找尋影響零售業銷售的因子。實驗的結果發現，所提出的方法得以同時考量分群及分類法的分析品質，並同時進行關鍵資料欄位的選取。

Clustering and classification are two major data mining techniques for exploring the hidden structures of data and classifying the unknown data instance, respectively. Due to the nature of difference between unsupervised and supervised methods, clustering and classification are applied separately for different analysis applications depending on whether the data label is available. In this research, a multi-objective framework combining clustering and classification called non-dominated sorting generic algorithm for combining clustering and classification (NSGA-CCC) was proposed to analyze different portions of a dataset. The clustering method is expected to help on rapidly analyzing or identifying the performance measures (Q dataset). The clustering results, labels, are then combined with other information (X dataset) as the inputs of the classification model which classifies the clustering labels by using X dataset. The non-dominated sorting generic algorithm-II (NSGA-II) was integrated in the framework to achieve optimal number of clusters. In this research, hierarchical clustering and decision tree are used for combining clustering and classification as an example. The point of sales (POS) dataset was used as a case study to evaluate NSGA-CCC and investigate the performance of retailing stores. The experimental result shows that NSGA-CCC can achieve the promising performance on finding the best number of cluster considering multi-objectives of clustering and classification and also performing the features selection simultaneously.

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