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From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others. 2. Hierarchical Clustering 2.1. Linkage Metrics 2.2. Hierarchical Clusters of Arbitrary Shapes 2.3. Binary Divisive Partitioning 2.4. Other Developments 3. Partitioning Relocation Clustering 3.1. Probabilistic Clustering 3.2. K-Medoids Methods 3.3. K-Means Methods 4. Density-Based Partitioning 4.1. Density-Based Connectivity 4.5. Density Functions 5. Grid-Based Methods 6. Co-Occurrence of Categorical Data 7. Other Clustering Techniques 7.1.
Constraint-Based Clustering 7.2. Relation to Supervised Learning 7.3. Gradient Descent and Artificial Neural Networks
7.4. Evolutionary Methods 7.5. Other Developments. This survey’s emphasis is on clustering in data mining. Such clustering is characterized by large datasets with many attributes of different types.
Tags : hierarchical clusters, gradient descent, arbitrary shapes, data mining techniques, artificial neural networks, density functions, evolutionary methods, computational biology, hidden patterns, k means, survey content, data exploration, hierarchical clustering, categorical data, spatial database
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