Clustering the Labeled and Unlabeled Datasets Using New MST Based Divide and Conquer Technique

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Provided by: International Journal of Computer Science & Engineering Technology (IJCSET)
Topic: Data Management
Format: PDF
Clustering is the process of partitioning the data set into subsets called clusters, so that the data in each subset share some properties in common. Clustering is an important tool to explore the hidden structures of modern large databases. Because of the huge variety of the problems and data distributions, different classical clustering algorithms, such as hierarchical, partitional, density-based and model-based clustering approaches, have been developed and no techniques are completely satisfactory for all the cases. Sufficient empirical evidences have shown that a New Minimum Spanning Tree (NMST) representation is quite invariant to the detailed geometric changes in cluster boundaries.
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