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Traditional k-means algorithm cannot get high clustering precise rate, and easily be affected by clustering center random initialized and isolated points, but the algorithm is simple with low time complexity, and can process the big data set quickly. This paper proposes an improved k-means algorithm named PKM. PKM is based on similarity degree among data points made by cumulated K-means, and get the...
Trajectory clustering is attractive for the task of class identification in spatial database. Existing trajectory clustering algorithm TRCLUS uses global parameters to discover common trajectories. However, it can not discover small and dense clusters and be sensitive to two input parameters. Based on the partition-and-group framework, we propose a simple but effective trajectory clustering algorithm...
Clustering is an important task in data mining with numerous applications, including minefield detection, seismology, astronomy, etc. At present, the academic communities have introduced various clustering algorithms, and these methods have been widely applied to different fields according to their respective characteristics. In this paper, we propose a novel clustering algorithm based on symmetric...
Density-based clustering and density-based outlier detection have been extensively studied in the data mining. However, Existing works address density-based clustering or density-based outlier detection solely. But for many scenarios, it is more meaningful to unify density-based clustering and outlier detection when both the clustering and outlier detection results are needed simultaneously. In this...
Existing trajectory clustering algorithm TRACLUS uses global parameters, it can not distinguish small, close, and dense trajectory clusters from large and sparse trajectory clusters. Moreover, TRACLUS needs two input parameters and is sensitive to input parameters. To avoid the shortcomings of TRACLUS, a neighborhood-based trajectory clustering algorithm named NBTC is proposed based on the improved...
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