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Many existing clustering algorithms use a single prototype to represent a cluster. However sometimes it is very difficult to find a suitable prototype for representing a cluster with an arbitrary shape. One possible solution is to employ multi-prototype instead. In this paper, we propose a minimum spanning tree (MST) based multi-prototype clustering algorithm. It is a split and merge scheme. In the...
According to the problem that K-Means clustering algorithm fails to correctly distinguish non-convex shape clusters, computation mode of distance in the algorithm is changed and density metric mode which can reflect the characteristics of data themselves is adopted instead. In the mode, Delaunay triangulation graph which has the advantages of nearest neighbour and adjacency is introduced to compute...
Spatial data mining is the process of identifying or extracting efficient, novel, potentially useful and ultimately understandable patterns from the spatial data set, the spatial clustering analysis is one of the most important research directions in spatial data mining. Clustering criterion implied in massive data can be discovered by spatial clustering analysis method which can be used to explore...
K-means clustering is sensitive to starting points and its time cost is expensive for large scale of data, such as audio. Sampling approach is widely applied to find “better” starting points for speeding up the clustering converging procedure. However, how to choose a reasonable sampling-rate remains a problem. In this paper, we reported our initial exploration of locating reasonable sampling-rates...
Clustering is a hot research field in data mining. There are so many methods or algorithms designed for different type data set on which data analysis action operates. Local Agglomerative Characteristic (LAC) based Algorithm, in this paper, is presented for data clustering, which can handle clusters of different size, shapes, and densities, can work well on different distributed and natural variant...
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