融合异常检测与区域分割的高效K-means聚类算法OA北大核心CSTPCD

In the traditional K-means and many improved algorithms,the inability to explicitly handle outliers,re-sulted in their poor clustering performance.To solve this problem,in this paper,an efficient K-means with region segment and outlier detection was proposed.Firstly,to obtain better clustering results,an unified clustering model to form an interactive collaboration between outlier detection and clustering was constructed.Secondly,to improve algorithm efficiency,clusters were adaptively segmented through near neighbor clusters search to reduce redundant calculations.Finally,on synthetic datasets and real datasets were tested to verify the effectiveness of the proposed method.The experimental results showed that EK-means algorithm outperformed other algorithms in terms of cluste-ring performance and execution efficiency.The ACC could reach 0.911 in the Wine dataset.