In the K-means clustering algorithm, each data point is uniquely placed into one category. The clustering quality is heavily dependent on the initial cluster centroid. Different initializations can yield varied result...In the K-means clustering algorithm, each data point is uniquely placed into one category. The clustering quality is heavily dependent on the initial cluster centroid. Different initializations can yield varied results; local adjustment cannot save the clustering result from poor local optima. If there is an anomaly in a cluster, it will seriously affect the cluster mean value. The K-means clustering algorithm is only suitable for clusters with convex shapes. We therefore propose a novel clustering algorithm CARDBK—＂centroid all rank distance（CARD）＂ which means that all centroids are sorted by distance value from one point and ＂BK＂ are the initials of ＂batch K-means＂—in which one point not only modifies a cluster centroid nearest to this point but also modifies multiple clusters centroids adjacent to this point, and the degree of influence of a point on a cluster centroid depends on the distance value between this point and the other nearer cluster centroids. Experimental results showed that our CARDBK algorithm outperformed other algorithms when tested on a number of different data sets based on the following performance indexes： entropy, purity, F1 value, Rand index and normalized mutual information（NMI）. Our algorithm manifested to be more stable, linearly scalable and faster.展开更多
基金Supported by the Social Science Foundation of Shaanxi Province of China(2018P03)the Humanities and Social Sciences Research Youth Fund Project of Ministry of Education of China(13YJCZH251)
文摘In the K-means clustering algorithm, each data point is uniquely placed into one category. The clustering quality is heavily dependent on the initial cluster centroid. Different initializations can yield varied results; local adjustment cannot save the clustering result from poor local optima. If there is an anomaly in a cluster, it will seriously affect the cluster mean value. The K-means clustering algorithm is only suitable for clusters with convex shapes. We therefore propose a novel clustering algorithm CARDBK—＂centroid all rank distance（CARD）＂ which means that all centroids are sorted by distance value from one point and ＂BK＂ are the initials of ＂batch K-means＂—in which one point not only modifies a cluster centroid nearest to this point but also modifies multiple clusters centroids adjacent to this point, and the degree of influence of a point on a cluster centroid depends on the distance value between this point and the other nearer cluster centroids. Experimental results showed that our CARDBK algorithm outperformed other algorithms when tested on a number of different data sets based on the following performance indexes： entropy, purity, F1 value, Rand index and normalized mutual information（NMI）. Our algorithm manifested to be more stable, linearly scalable and faster.
