About: Objective Criteria for the Evaluation of Clustering Methods

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article from 1971

Attributes	Values
rdf:type	Item
description	ലേഖനം (ml) wetenschappelijk artikel (nl) наукова стаття, опублікована в грудні 1971 (uk) im Dezember 1971 veröffentlichter wissenschaftlicher Artikel (de) article from 1971 (en)
publication date	wds:Q56388104-CA1C0D76-77C0-4A0E-AAE0-BECE45F7A30F
publication date	1971-12-01 00:00:00Z (xsd:dateTime)
language of work or name	wds:Q56388104-7A067E0C-0716-4C78-BC29-395602BF8FE6
language of work or name	English
author name string	wds:Q56388104-86F85F57-E087-4133-BA5D-484F4F4DB23F
author name string	William M. Rand
rdfs:label	Objective Criteria for the Evaluation of Clustering Methods (en) Objective Criteria for the Evaluation of Clustering Methods (nl)
skos:prefLabel	Objective Criteria for the Evaluation of Clustering Methods (en) Objective Criteria for the Evaluation of Clustering Methods (nl)
name	Objective Criteria for the Evaluation of Clustering Methods (en) Objective Criteria for the Evaluation of Clustering Methods (nl)
title	wds:Q56388104-A019A0F4-92CA-48F3-9450-B5B1C0900BA8
title	Objective Criteria for the Evaluation of Clustering Methods (en)
page(s)	wds:Q56388104-40DF3824-0418-418A-AACB-0C1D0FFA007A
page(s)	846
instance of	wds:Q56388104-8B0C0E7C-746A-41FE-B093-05ECDD66DFBC
instance of	scholarly article
published in	wds:Q56388104-9E1EF078-84AD-40D2-BEA1-ACE59D72B5A1
published in	Journal of the American Statistical Association
issue	wds:Q56388104-74B203CE-8EAE-4959-96C0-C65D6EE4B6F4
volume	wds:Q56388104-ED40D356-C8BD-4DFB-8AA3-C03007A16469
issue	336
volume	66
DOI	wds:Q56388104-59E81650-B3B7-4288-BFDE-C00E19B49D46
DOI	http://dx.doi.org/10.2307/2284239
DOI	10.2307/2284239
JSTOR article ID	wds:Q56388104-89790181-0D45-4067-BDE0-4263828B4F4A
JSTOR article ID	2284239
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q56388104
is cites work of	Analysis of community structure in networks of correlated data Robustness of community structure in networks. Distributed cell assemblies for general lexical and category-specific semantic processing as revealed by fMRI cluster analysis Intracellular signalling and intercellular coupling coordinate heterogeneous contractile events to facilitate tissue folding. Comparison of different HCV viral load and genotyping assays Computational cluster validation in post-genomic data analysis Complex modular structure of large-scale brain networks. Determinants of HIV-1 broadly neutralizing antibody induction. The influence of clan structure on the genetic variation in a single Ghanaian village. Robust clustering using exponential power mixtures. Spectral preprocessing for clustering time-series gene expressions Anatomy of news consumption on Facebook Characterizing leader sequences of CRISPR loci Variable selection in penalized model-based clustering via regularization on grouped parameters. Genealogical lineage sorting leads to significant, but incorrect Bayesian multilocus inference of population structure. Molecular Evolution of the Neural Crest Regulatory Network in Ray-Finned Fish. A novel approach for data integration and disease subtyping. High-dimensional genomic data bias correction and data integration using MANCIE Robust Clustering Method in the Presence of Scattered Observations Fuzzyc-Means and Cluster Ensemble with Random Projection for Big Data Clustering Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods. Knowing when you're drunk: A cluster analysis of cues to intoxication Research on Multifeature Segmentation Method of Remote Sensing Images Based on Graph Theory Compiling a Suitable Level of Sense Granularity in a Lexicon for AI Purposes: The Open Source COR Lexicon A scalable kernel-based semisupervised metric learning algorithm with out-of-sample generalization ability. Information-maximization clustering based on squared-loss mutual information Alcoholics' perceptions of cues to intoxication. Bayesian-OverDBC: A Bayesian Density-Based Approach for Modeling Overlapping Clusters Evaluating cluster preservation in frequent itemset integration for distributed databases
is cites work of	wds:Q38380656-615EF95E-1F4F-4C8C-880C-9FDD41DBFC9E wds:Q51897815-967A6FCA-A7D5-41D2-A33F-5F3B54B7A842 wds:Q37730138-EAAFD67E-BE43-48A3-BFE7-A54ABE51826E wds:Q39948305-6F9A64D3-0408-44AD-8D2C-EFC92FC4D342 wds:Q39429375-DC6485A1-61E6-4A81-A1EB-8C8687224E47 wds:Q57607315-43737070-01E0-4A7A-BED6-DC5340DA5128 wds:Q50616139-2CC08BFE-E44C-497B-904F-530E89070E58 wds:Q48545697-4D3AA9CA-1A52-4FED-AC1C-557BFB5DCF6C wds:Q50702752-C7BE1559-8C40-4880-8692-B688F278CEA9 wds:Q59130091-2DBC5E30-FE5C-4253-942D-32649AFD05EF wds:Q34886197-DD36EFF8-09EE-4A0C-81CD-4F48371ED669 wds:Q35209076-93641119-D4A7-4307-A34C-06ED62A67802 wds:Q47748212-05295731-8425-4970-9A5D-6A942CD6108E wds:Q45917114-94431C2D-3FE5-4821-B106-865B69847942 wds:Q30990574-886F84B8-9944-46F6-8C3C-80142686B12B wds:Q40422255-4C59EDFF-7349-42FE-A923-1FDDDE52631A wds:Q52203353-A669E443-0C06-4BBE-92C0-C15C33E2B6DA wds:Q59117529-A65697E3-4826-4C86-BF47-FBADA209367F wds:Q115497766-e573d71a-445b-40b9-d401-fa3c50ae655a wds:Q31075558-7F09C4FE-1773-4901-85FF-9FD2CE56E602 wds:Q30651812-49EA5C88-4D14-4403-BC89-AFEABCE1BF31 wds:Q37186014-863862CA-CACD-46F9-93DF-C6CDF13B8EF5 wds:Q40476608-09271404-EFAA-4A12-850A-FADAB33FBA65 wds:Q46134842-0B171975-EFB0-4926-9626-61D0680EA7E1 wds:Q47234937-91A5F8C9-1F9B-4995-889D-2066FE4C992E wds:Q45909320-9DAC5531-3F06-4023-8A9E-694A58614D82 wds:Q33491145-5B6772BA-E54E-41E1-9CB2-DD7B028A9F69 wds:Q45032024-E4280A5B-7A2E-4EAA-B8CD-D08C51EE5A3F wds:Q43046996-CE4416B9-42AD-434D-A2DA-211A2822A1E1

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