RK-Means Clustering: K-Means with Reliability
RK-Means Clustering: K-Means with Reliability
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DOI：
10.1093/ietisy/e91-d.1.96
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
C. Hua;Qian Chen;Haiyuan Wu;T. Wada
中科院分区：
文献类型：
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作者：
C. Hua;Qian Chen;Haiyuan Wu;T. Wada
本文提出了一种 RK-means 聚类算法，该算法通过在 K-means 聚类算法中引入新的可靠性评估来实现可靠的数据分组。传统的K-means聚类算法有两个缺点：1）如果假设的簇数不正确，聚类结果将变得不可靠； 2）在簇中心更新过程中，属于该簇的所有数据点都被平等地使用，而不考虑它们与簇中心的距离有多远。在本文中，我们通过考虑每个数据点与其最近的两个聚类中心之间的三角关系，为K均值聚类算法引入了一种新的可靠性评估。我们应用所提出的算法来跟踪视频序列中的对象，并证实了其有效性和优势。
This paper presents an RK-means clustering algorithm which is developed for reliable data grouping by introducing a new reliability evaluation to the K-means clustering algorithm. The conventional K-means clustering algorithm has two shortfalls: 1) the clustering result will become unreliable if the assumed number of the clusters is incorrect; 2) during the update of a cluster center, all the data points belong to that cluster are used equally without considering how distant they are to the cluster center. In this paper, we introduce a new reliability evaluation to K-means clustering algorithm by considering the triangular relationship among each data point and its two nearest cluster centers. We applied the proposed algorithm to track objects in video sequence and confirmed its effectiveness and advantages.