本研究的目標在發展一個資料分析方法來解析多準則決策模型(Non Dominated Sorting Genetic Algorithm – Sequential Clustering Classification, NSGAII-SCC) 所產生之分群及分類的結果，以提供該模型使用者較佳之欄位選取(feature selection)。NSGAII-SCC針對不同的資料集：效能資料(Q資料)及與效能相關資料(X資料)來進行分群以及分類，其目的在於選擇適當的欄位以同時維持資料分群的密集性(compactness)及資料分類的預測準確度(accuracy)之品質。然而先前的研究多著重於選取最佳分群及分類結果作為資料分析時的參考，本研究則著重於進一步分析基因演算最佳化過程中所產生之演化資訊。透過逐步迴歸方法消除多餘欄位，並使用資料分群來探索欄位選擇對分群及分類結果之影響。實驗結果發現，本研究所提出之方法能進一步將NSGAII-SCC模型的運算結果進行更精確的解析，以提升NSGAII-SCC在資料分析的能力。

This research aims to develop a data analysis framework to analyze the data generated by performing clustering and classification sequentially. The clustering and classification procedures are conducted on two different types of data, respectively: one contains the performance measure dataset denoted as Q dataset; the other contains the factors or relevant information regarding the clustering result, denoted as X dataset. Non Dominated Sorting Genetic Algorithm – Sequential Clustering Classification (NSGAII-SCC) with stepwise regression was proposed to investigate the multiple solutions generated from the proposed NSGA which aims to optimize the compactness of clustering and accuracy of classification. In this research, the stepwise regression model and frequency-based similarity matrix were applied to eliminate the redundant features in the datasets and identify the significant features for classification, respectively. The experiment result shows that the proposed methods are able to identify the redundant features and also provide the useful information for the evaluation of clustering and classification results.

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