Algorithms for clustering data
Algorithms for clustering data
Location- and Density-Based Hierarchical Clustering Using Similarity Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Algorithms
A New Cluster Isolation Criterion Based on Dissimilarity Increments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bagging for Path-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient line symmetry-based K-means algorithm
Pattern Recognition Letters
Faster and more robust point symmetry-based K-means algorithm
Pattern Recognition
Minimax partial distortion competitive learning for optimal codebook design
IEEE Transactions on Image Processing
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We present a new clustering algorithm (NPSK algorithm), based on the modification of the newly developed point symmetry-based K-means algorithm (PSK algorihtm). Not only the proposed NPSK algorithm is suitable for almost all test data sets used by Chung and Liu for PSK algorihtm, the proposed NPSK algorithm is also suitable for the case where the symmetric property of the data set is not so distinct. Experimental results demonstrate that our proposed NPSK algorithm is rather encouraging.