Comparing clusterings: an axiomatic view

By Marina Meila. International Conference on Machine Learning, 2005

686 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/102ae5a579538aa00569553c3151578561bc8b6f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Signal Processing

Citations in the corpus

Community detection in networks: A user guide (2016, background): “Adjusted indices have also the disadvantage of nonlocality (Meilă, 2005): the similarity between partitions differing only in one region of the network depends on how the remainder of the network is subdivided.”
Robustness of community structure in networks. (2007, methodology): “In our work we make use of a different information theoretic measure, the variation of information [31, 33, 36].”
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering (2010, neutral): “Somer’s Gamma [24] Completeness [38] Ratio of Repetition [24] Homogeneity [38] Modified Ratio of Repetition [24] Variation of information [29] Adjusted Ratio of Clustering [24] Mutual information [14]”
Segmentation of Natural Images by Texture and Boundary Compression (2010, positive): “To quantitatively evaluate the performance of our method, we use three metrics for comparing pairs of image segmentations: the probabilistic Rand index (PRI) (Rand (1971)), the variation of information (VOI) (Meila (2005)), and the global F-measure (Arbelaez (2006)): 1.”
Ensembles for unsupervised outlier detection: challenges and research questions a position paper (2014, methodology): “Other similarity measures compare partitions by mapping sets, important examples being entropy-based measures such as (normalized) mutual information [67; 48].”
A survey of graph theoretical approaches to image segmentation (2013, methodology): “Meila [131] proposed an information-theoretic distance of clustering.”
Learning full pairwise affinities for spectral segmentation (2010, methodology): “Variation of Information (VI): The Variation of Information score was introduced for the purpose of clustering comparison [42].”
Introduction to Information Retrieval (2008, neutral): “internal measure and give evidence that it is a good predictor of the quality of a clustering. Kleinberg (2002) and Meilă (2005) present axiomatic frameworks for comparing clusterings.”
Comparing clusterings---an information based distance (2007, neutral): “the second kind S(n,K) [17].”
Layered label propagation: a multiresolution coordinate-free ordering for compressing social networks (2010, methodology): “The second measure that we have decided to employ to compare partitions is the Variation of Information (VI) proposed in [16].”
Information theoretic measures for clusterings comparison: is a correction for chance necessary? (2009, methodology): “(Meilǎ, 2005) suggested using the socalled Variation of Information (VI), which she proved to be a true metric on the space of clusterings:”
Segmentation using superpixels: A bipartite graph partitioning approach (2012, methodology): “[15, 9, 28]) to use the four criteria: 1) Probabilistic Rand Index (PRI) [27], measuring the likelihood of a pair of pixels being grouped consistently in two segmentations; 2) Variation of Information (VoI) [19], computing the amount of information of one result not contained in the other; 3) Global”
Unsupervised segmentation of natural images via lossy data compression (2008, background): “(2) The Variation of Information (VoI) metric [22] defines the distance between two segmentations as the average conditional entropy of one segmentation given the other, and thus roughly measures the amount of randomness in one segmentation which cannot be explained by the other.”