Characterization and evaluation of similarity measures for pairs of clusterings

Pfitzner, Darius; Leibbrandt, Richard; Powers, David

doi:10.1007/s10115-008-0150-6

Cited by 184 publications

(134 citation statements)

References 37 publications

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“…The functions that measure the similarity between partitions are called in many different ways, such as external cluster validity indices or partition similarity measures. In the rest of the paper we will call them similarity measures as Pfitzner et al (2009) …”

Section: An Alternative CVI Evaluation Methodologymentioning

confidence: 99%

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Towards a standard methodology to evaluate internal cluster validity indices

Gurrutxaga

Muguerza

Arbelaitz

et al. 2011

Pattern Recognition Letters

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The evaluation and comparison of internal cluster validity indices is a critical problem in the clustering area. The methodology used in most of the evaluations assumes that the clustering algorithms work correctly. We propose an alternative methodology that does not make this often false assumption. We compared 7 internal cluster validity indices with both methodologies and concluded that the results obtained with the proposed methodology are more representative of the actual capabilities of the compared indices.

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Section: An Alternative CVI Evaluation Methodologymentioning

confidence: 99%

“…(Meilȃ, 2005). More information about similarity measures can be found in (Albatineh et al, 2006;Batagelj and Bren, 1995;Baulieu, 1989;Pfitzner et al, 2009). …”

Section: An Alternative CVI Evaluation Methodologymentioning

confidence: 99%

Towards a standard methodology to evaluate internal cluster validity indices

Gurrutxaga

Muguerza

Arbelaitz

et al. 2011

Pattern Recognition Letters

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“…If groups or clusterings are being compared, and there is not even a known or fixed number of classes, then there are a large number of clustering comparison techniques to choose from (Pfitzner et al 2009). …”

Section: Evaluation and Statisticsmentioning

confidence: 99%

A critical time in computational cognitive science

Powers

2015

Comput Cogn Sci

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Understanding how people tick is an endeavour that has challenged us for millennia, both in informal settings and in increasingly formalized and scientific disciplines. Some are interested in the biology and others the behaviour. Some are focussed on language and others on culture or emotion. Some approach this as pure science worthy of understanding in its own right. Some approach it as applied science with value born of practical applications that improve our lifestyle and characterize our modern technological society. Cognitive Science was born as Psychologists and Linguists found that the assumptions and predictions of their theories and models were spilling outside their discipline, as Neuroscience provided a neural substrate for models of perception and cognition that obviated the need for the postulation of hypothetical daemons, as Computer Science and Mathematics provided computational tools for testing models and theories that couldn't be tested empirically in the real world. Artificial Intelligence was also born in such an environment, with the early researchers exploring models of intelligence as much as developing intelligent programs. But Computational Intelligence and Cognitive Science increasingly reflect a contrast between an applied aim of building intelligent applications and entities, and a pure aim of understanding existing intelligent entities and functions, and we seem to lack a bridge between them. Computational Cognitive Science aims to provide this bridge. Introduction and scope

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“…Pfitzner et al (2008) presented a comparison of clustering evaluation measures (IT based and others). While their analysis is extensive, their experiments were confined to artificial data.…”

Section: Previous Evaluation Measuresmentioning

confidence: 99%

The NVI clustering evaluation measure

Reichart

Rappoport

2009

Proceedings of the Thirteenth Conference on Computational Natural Language Learning - CoNLL '09

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Clustering is crucial for many NLP tasks and applications. However, evaluating the results of a clustering algorithm is hard. In this paper we focus on the evaluation setting in which a gold standard solution is available. We discuss two existing information theory based measures, V and VI, and show that they are both hard to use when comparing the performance of different algorithms and different datasets. The V measure favors solutions having a large number of clusters, while the range of scores given by VI depends on the size of the dataset. We present a new measure, NVI, which normalizes VI to address the latter problem. We demonstrate the superiority of NVI in a large experiment involving an important NLP application, grammar induction, using real corpus data in English, German and Chinese.

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Characterization and evaluation of similarity measures for pairs of clusterings

Cited by 184 publications

References 37 publications

Towards a standard methodology to evaluate internal cluster validity indices

Towards a standard methodology to evaluate internal cluster validity indices

A critical time in computational cognitive science

The NVI clustering evaluation measure

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