本研究的目標旨在利用主成分分析維度縮減方法，提升非凌駕式排序基因演算法連續分群分類資料分析架構 (Non-Dominated Sorting Genetic Algorithm – Sequential Clustering Classification, NSGAII-SCC) 的分析結果品質。NSGAII-SCC將收集的資料依照其包含的屬性特徵區分成代表效能的資料 (Q資料)，以及與效能相關的資訊 (X資料)，並分別對Q資料及X資料進行連續分群分類資料分析。此資料分析架構目的在於選擇適當的屬性進行連續分群分類流程同時，維持資料分群的密集性 (compactness) 和資料分類的準確度 (accuracy) 之品質。過去對於NSGAII-SCC的研究多著重於選取對於連續分群分類有最佳結果的屬性組合，本研究則透過加入維度縮減的機制改善原本NSGAII-SCC資料分析架構的分析結果品質，並試圖探索維度縮減方法應用在NSGAII-SCC架構下的各種情境。實驗結果證明本研究所提出的方法不僅顯著增加分析結果品質，同時還能維持不亞於原NSGAII-SCC的運算速度。

This research aims to improve the performance of the Non-Dominated Sorting Genetic Algorithm – Sequential Clustering Classification (NSGAII-SCC) by integrating a dimensionality reduction technique which is the Principal Component Analysis (PCA). The NSGAII-SCC separates a dataset into two subsets. Next it applies a clustering method to generate labels from a sub-dataset which contains performance related features. Then, the labels generated are used as prediction targets in a classification model to classify data in the second sub-dataset. The sequential clustering and classification (SCC) mechanism is integrated with a feature selection mechanism which is the genetic algorithm (GA). Through the NSGA-II, the compactness of clustering and the prediction accuracy of classification will be simultaneously optimized. Instead of finding the best combination of clustering and classification methods like most studies in the past, this research aims to improve the NSGAII-SCC algorithm performance by integrating a dimensionality reduction technique. The experiment result shows that the proposed method performed better than the original NSGAII-SCC in terms of the solution quality.

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