Journal ArticleDOI
A Method for Comparing Two Hierarchical Clusterings
TLDR
The derivation and use of a measure of similarity between two hierarchical clusterings, Bk, is derived from the matching matrix, [mij], formed by cutting the two hierarchical trees and counting the number of matching entries in the k clusters in each tree.Abstract:
This article concerns the derivation and use of a measure of similarity between two hierarchical clusterings. The measure, Bk , is derived from the matching matrix, [mij ], formed by cutting the two hierarchical trees and counting the number of matching entries in the k clusters in each tree. The mean and variance of Bk are determined under the assumption that the margins of [mij ] are fixed. Thus, Bk represents a collection of measures for k = 2, …, n – 1. (k, Bk ) plots are found to be useful in portraying the similarity of two clusterings. Bk is compared to other measures of similarity proposed respectively by Baker (1974) and Rand (1971). The use of (k, Bk ) plots for studying clustering methods is explored by a series of Monte Carlo sampling experiments. An example of the use of (k, Bk ) on real data is given.read more
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Journal ArticleDOI
Community detection in graphs
TL;DR: A thorough exposition of community structure, or clustering, is attempted, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists.
Journal ArticleDOI
Community detection in graphs
TL;DR: A thorough exposition of the main elements of the clustering problem can be found in this paper, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
Journal ArticleDOI
Comparing clusterings---an information based distance
TL;DR: This paper proposes an information theoretic criterion for comparing two partitions, or clusterings, of the same data set, called variation of information (VI), and presents it from an axiomatic point of view, showing that it is the only ''sensible'' criterion for compare partitions that is both aligned to the lattice and convexely additive.
Journal ArticleDOI
Time-series clustering - A decade review
TL;DR: This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time- series approaches during the last decade and enlighten new paths for future works.
References
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Journal ArticleDOI
Objective Criteria for the Evaluation of Clustering Methods
TL;DR: This article proposes several criteria which isolate specific aspects of the performance of a method, such as its retrieval of inherent structure, its sensitivity to resampling and the stability of its results in the light of new data.