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Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters in 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, marketing, medical diagnostics, computational biology and many others.
Clustering is the subject of active research in several fields such as statistics, pattern recognition and machine learning. Clustering is the classification of similar objects into different group. We can also define clustering is the unsupervised learning of a hidden data concept. Besides the term data clustering, there are a number of terms with similar meanings, including cluster analysis, automatic classification, numerical taxonomy, and typological analysis.
Types of Clustering
Categorization of clustering algorithms is neither straight forward nor canonical. Data clustering algorithms can be hierarchical or partitional. Two-way clustering, co-clustering or bi-clustering are the names for clustering where not only the objects are clustered but also the features of the objects. We provide a classification of clustering algorithms listed below.
Different Clustering Algorithms
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