隨著科技在現今社會應用的普及，大量的數據可以被快速又準確的收集並紀錄，使得日常生活中充斥著各式各樣的數據，而如何妥善地對這些數據進行妥善的處理與分析，並從中得到有價值的訊息成為了一項備受矚目的議題。在資料探勘的領域內，分群分析是一項非常重要的技術，不同的數據型態可以用不同的方法著手分析，但二進制數據的分群方法明顯的少於類別型和數值型資料的分群方法。本研究針對二進制高維稀疏之資料進行分群，提出稀疏二進制降維方法(BDR)結合模糊K-眾數演算法(FKM)來改善分群結果，再進一步以遺傳演算法(GA)為基礎之模糊K-眾數與稀疏二進制降維方法的應用優化分群的表現。
在本研究中的實驗使用了九組高維稀疏的二進制資料作為標竿資料集，分別針對提出的演算法進行分析並使用準確率(accuracy)、召回率(recall)、精準度(precision)及F1-分數(F1-score)四種評比指標以及兩種適應度評比方法與提出的方法BDR-GAFKM和現有的分群方法進行比較，而根據實驗結果可以證實，BDR-GAFKM在各個指標中都有較優異的表現。此外除了標竿資料集。此外，本研究亦進行個案分析，分析結果顯示，本研究提出的方法BDR-GAFKM的適應值皆優於FKM、BDR-FKM和GAFKM的適應值，可證實本研究提出之方法聚類效果優於其他方法。

With the popularization of technology in today's society, a large amount of data can be quickly and accurately collected and recorded, making daily life full of various data, and how to properly process and analyze these data, And getting valuable information from it has become a high-profile issue. In the field of data mining, cluster analysis is a very important technique. Different data types can be analyzed in different ways, but the clustering method of binary data is obviously less than that of categorical and numerical data.
In this study, for clustering binary high-dimensional sparse data, a sparse binary dimensionality reduction method (BDR) combined with a fuzzy K-modes algorithm (FKM) is proposed to improve the clustering results, and further a fuzzy K-modes based on genetic algorithm (GA) is also proposed. Application of sparse binary dimensionality reduction methods is able to optimize clustering performance.
The experiments in this study used nine benchmark data sets with highly sparse binary data and analyzed the proposed algorithm using accuracy, recall, precision, and F1-score. Four evaluation indicators and two fitness evaluation methods are used to compare the proposed method, BDR-GAFKM, with some existing clustering methods. According to the experimental results, it can be confirmed that BDR-GAFKM has better performance than the other algorithms in terms of four indicators.
In addition, a case study is also carried out. The analysis results show that the fitness value of the proposed method, BDR-GAFKM, is better than those of FKM, BDR-FKM and GAFKM, which can confirm the clustering effect of the proposed method is better than the other methods in this study.

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