Algorithms for clustering data
Algorithms for clustering data
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical grid-based clustering over data streams
ACM SIGMOD Record
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Enhancing minimum spanning tree-based clustering by removing density-based outliers
Digital Signal Processing
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In this paper a clustering algorithm based on the minimum spanning tree (MST) with neighborhood density difference estimation is proposed. Neighborhood are defined by patterns connected with the edges in the MST of a given dataset. In terms of the difference between patterns and their neighbor density, boundary patterns and corresponding boundary edges are detected. Then boundary edges are cut, and as a result the dataset is split into defined number clusters. For reducing time complexity of detecting boundary patterns, an rough and a refined boundary candidates estimation approach are employed, respectively. The experiments are performed on synthetic and real data. The clustering results demonstrate the proposed algorithm can deal with not well separated, shape-diverse clusters.