Abstract
Hierarchical clustering builds a binary hierarchy on the entity set. The Chapter’s material explains an algorithm for agglomerative clustering and two different algorithms for divisive clustering, all three based on the same square error criterion as K-Means partitioning method. Agglomerative clustering starts from a trivial set of singletons and merges two clusters at a time. Divisive clustering splits clusters in parts and should be a more interesting approach computationally because it can utilize fast splitting algorithms and, also, stop splitting whenever it seems right. One divisive algorithm proceeds with the conventional K-Means at K = 2 utilized for splitting a cluster. The other maximizes summary association coefficient to make splits conceptually, that is, using one feature at a time. The last section is devoted to the Single Link clustering, a popular method for extraction of elongated structures from the data. Relations between single link clustering and two popular graph-theoretic structures, the Minimum Spanning Tree (MST) and connected components, are explained.
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Mirkin, B. (2011). Hierarchical Clustering. In: Core Concepts in Data Analysis: Summarization, Correlation and Visualization. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-0-85729-287-2_7
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DOI: https://doi.org/10.1007/978-0-85729-287-2_7
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