
@article{strehl_cluster_2003,
	title = {Cluster {Ensembles} — a {Knowledge} {Reuse} {Framework} for {Combining} {Multiple} {Partitions}},
	volume = {3},
	issn = {1532-4435},
	journal = {J. Mach. Learn. Res.},
	author = {Strehl, Alexander and Ghosh, Joydeep},
	year = {2003},
	keywords = {cluster analysis, clustering, consensus functions, ensemble, knowledge reuse, multi-learner systems, mutual information, partitioning, unsupervised learning},
	pages = {583--617},
	file = {[Strehl.2003] Cluster Ensembles — a Knowledge Reuse Framework for Combining Multiple.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/3.InfoTheory/[Strehl.2003] Cluster Ensembles — a Knowledge Reuse Framework for Combining Multiple.pdf:application/pdf}
}

@article{meila_comparing_2007,
	title = {Comparing clusterings—an information based distance},
	volume = {98},
	issn = {0047259X},
	language = {en},
	number = {5},
	journal = {Journal of Multivariate Analysis},
	author = {Meilă, Marina},
	month = may,
	year = {2007},
	pages = {873--895},
	file = {[Meilă.2007] Comparing clusterings—an information based distance.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/[Meilă.2007] Comparing clusterings—an information based distance.pdf:application/pdf}
}

@incollection{goos_comparing_2003,
	address = {Berlin, Heidelberg},
	title = {Comparing {Clusterings} by the {Variation} of {Information}},
	volume = {2777},
	isbn = {978-3-540-40720-1 978-3-540-45167-9},
	booktitle = {Learning {Theory} and {Kernel} {Machines}},
	publisher = {Springer Berlin Heidelberg},
	author = {Meilă, Marina},
	editor = {Goos, Gerhard and Hartmanis, Juris and van Leeuwen, Jan and Schölkopf, Bernhard and Warmuth, Manfred K.},
	year = {2003},
	pages = {173--187},
	file = {[Meilă.2003] Comparing Clusterings by the Variation of Information.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/3.InfoTheory/[Meilă.2003] Comparing Clusterings by the Variation of Information.pdf:application/pdf}
}

@techreport{wagner_comparing_2007,
	title = {Comparing {Clusterings} – {An} {Overview}},
	number = {2006-04},
	institution = {Department of Informatics},
	author = {Wagner, Silke and Wagner, Dorothea},
	year = {2007},
	file = {[Wagner.2007] Comparing Clusterings – An Overview.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/[Wagner.2007] Comparing Clusterings – An Overview.pdf:application/pdf}
}

@article{albatineh_similarity_2006,
	title = {On {Similarity} {Indices} and {Correction} for {Chance} {Agreement}},
	volume = {23},
	issn = {0176-4268, 1432-1343},
	language = {en},
	number = {2},
	journal = {Journal of Classification},
	author = {Albatineh, Ahmed N. and Niewiadomska-Bugaj, Magdalena and Mihalko, Daniel},
	month = sep,
	year = {2006},
	pages = {301--313},
	file = {[Albatineh.2006] On Similarity Indices and Correction for Chance Agreement.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/[Albatineh.2006] On Similarity Indices and Correction for Chance Agreement.pdf:application/pdf}
}

@article{mirkin_eleven_2001,
	title = {Eleven {Ways} to {Look} at the {Chi}-{Squared} {Coefficient} for {Contingency} {Tables}},
	volume = {55},
	issn = {0003-1305, 1537-2731},
	language = {en},
	number = {2},
	journal = {The American Statistician},
	author = {Mirkin, Boris},
	month = may,
	year = {2001},
	pages = {111--120},
	file = {[Mirkin.2001] Eleven Ways to Look at the Chi-Squared Coefficient for Contingency Tables.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/1.CountingPairs/[Mirkin.2001] Eleven Ways to Look at the Chi-Squared Coefficient for Contingency Tables.pdf:application/pdf}
}

@article{rand_objective_1971,
	title = {Objective {Criteria} for the {Evaluation} of {Clustering} {Methods}},
	volume = {66},
	issn = {01621459},
	number = {336},
	journal = {Journal of the American Statistical Association},
	author = {Rand, William M.},
	month = dec,
	year = {1971},
	pages = {846},
	file = {[Rand.1971] Objective Criteria for the Evaluation of Clustering Methods.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/1.CountingPairs/[Rand.1971] Objective Criteria for the Evaluation of Clustering Methods.pdf:application/pdf}
}

@inproceedings{kuncheva_using_2004,
	title = {Using diversity in cluster ensembles},
	volume = {2},
	isbn = {978-0-7803-8567-2},
	booktitle = {Proceedings of the {IEEE} {International} {Conference} on {Systems}, {Man} and {Cybernetics}},
	publisher = {IEEE},
	author = {Kuncheva, L.I. and Hadjitodorov, S.T.},
	year = {2004},
	pages = {1214--1219},
	file = {[Kuncheva.2004] Using diversity in cluster ensembles.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/1.CountingPairs/[Kuncheva.2004] Using diversity in cluster ensembles.pdf:application/pdf}
}

@article{fowlkes_method_1983,
	title = {A {Method} for {Comparing} {Two} {Hierarchical} {Clusterings}},
	volume = {78},
	issn = {0162-1459, 1537-274X},
	language = {en},
	number = {383},
	journal = {Journal of the American Statistical Association},
	author = {Fowlkes, E. B. and Mallows, C. L.},
	month = sep,
	year = {1983},
	pages = {553--569},
	file = {[Fowlkes.1983] A Method for Comparing Two Hierarchical Clusterings.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/1.CountingPairs/[Fowlkes.1983] A Method for Comparing Two Hierarchical Clusterings.pdf:application/pdf}
}

@techreport{dongen_performance_2000,
	address = {Amsterdam, The Netherlands, The Netherlands},
	title = {Performance {Criteria} for {Graph} {Clustering} and {Markov} {Cluster} {Experiments}},
	institution = {CWI (Centre for Mathematics and Computer Science)},
	author = {Dongen, Stijn},
	year = {2000},
	file = {[Dongen.2000] Performance Criteria for Graph Clustering and Markov Cluster Experiments.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/1.CountingPairs/[Dongen.2000] Performance Criteria for Graph Clustering and Markov Cluster Experiments.pdf:application/pdf}
}

@article{jaccard_distribution_1912,
	title = {{THE} {DISTRIBUTION} {OF} {THE} {FLORA} {IN} {THE} {ALPINE} {ZONE}.1},
	volume = {11},
	issn = {0028-646X, 1469-8137},
	language = {en},
	number = {2},
	journal = {New Phytologist},
	author = {Jaccard, Paul},
	month = feb,
	year = {1912},
	pages = {37--50},
	file = {[Jaccard.1912] THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/1.CountingPairs/[Jaccard.1912] THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.pdf:application/pdf}
}

@article{li_combining_2010,
	title = {On combining multiple clusterings: an overview and a new perspective},
	volume = {33},
	issn = {0924-669X, 1573-7497},
	shorttitle = {On combining multiple clusterings},
	language = {en},
	number = {2},
	journal = {Applied Intelligence},
	author = {Li, Tao and Ogihara, Mitsunori and Ma, Sheng},
	month = oct,
	year = {2010},
	pages = {207--219},
	file = {[Li.2010] On combining multiple clusterings.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/1.CountingPairs/[Li.2010] On combining multiple clusterings.pdf:application/pdf}
}

@inproceedings{larsen_fast_1999,
	title = {Fast and effective text mining using linear-time document clustering},
	isbn = {978-1-58113-143-7},
	language = {en},
	booktitle = {Proceedings of the fifth {ACM} {SIGKDD} international conference on {Knowledge} discovery and data mining},
	publisher = {ACM Press},
	author = {Larsen, Bjornar and Aone, Chinatsu},
	year = {1999},
	pages = {16--22},
	file = {[Larsen.1999] Fast and effective text mining using linear-time document clustering.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/2.SetOverlap/[Larsen.1999] Fast and effective text mining using linear-time document clustering.pdf:application/pdf}
}

@article{meila_experimental_2001,
	title = {An {Experimental} {Comparison} of {Model}-{Based} {Clustering} {Methods}},
	volume = {42},
	issn = {1573-0565},
	abstract = {We compare the three basic algorithms for model-based clustering on high-dimensional discrete-variable datasets. All three algorithms use the same underlying model: a naive-Bayes model with a hidden root node, also known as a multinomial-mixture model. In the first part of the paper, we perform an experimental comparison between three batch algorithms that learn the parameters of this model: the Expectation–Maximization (EM) algorithm, a “winner take all” version of the EM algorithm reminiscent of the K-means algorithm, and model-based agglomerative clustering. We find that the EM algorithm significantly outperforms the other methods, and proceed to investigate the effect of various initialization methods on the final solution produced by the EM algorithm. The initializations that we consider are (1) parameters sampled from an uninformative prior, (2) random perturbations of the marginal distribution of the data, and (3) the output of agglomerative clustering. Although the methods are substantially different, they lead to learned models that are similar in quality.},
	number = {1},
	journal = {Machine Learning},
	author = {Meilă, Marina and Heckerman, David},
	month = jan,
	year = {2001},
	pages = {9--29},
	file = {[Meilă.2001] An Experimental Comparison of Model-Based Clustering Methods.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/2.SetOverlap/[Meilă.2001] An Experimental Comparison of Model-Based Clustering Methods.pdf:application/pdf}
}

@book{cover_elements_2006,
	address = {Hoboken, N.J},
	edition = {2nd ed},
	title = {Elements of information theory},
	isbn = {978-0-471-24195-9},
	publisher = {Wiley-Interscience},
	author = {Cover, T. M. and Thomas, Joy A.},
	year = {2006},
	note = {OCLC: ocm59879802},
	keywords = {Information theory}
}

@inproceedings{ana_robust_2003,
	title = {Robust data clustering},
	volume = {2},
	isbn = {978-0-7695-1900-5},
	booktitle = {2003 {IEEE} {Computer} {Society} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
	publisher = {IEEE Comput. Soc},
	author = {Ana, L.N.F. and Jain, A.K.},
	year = {2003},
	pages = {II--128--II--133},
	file = {[Ana.2003] Robust data clustering.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/3.InfoTheory/[Ana.2003] Robust data clustering.pdf:application/pdf}
}

@article{wallace_comment_1983,
	title = {Comment},
	volume = {78},
	issn = {0162-1459, 1537-274X},
	language = {en},
	number = {383},
	journal = {Journal of the American Statistical Association},
	author = {Wallace, David L.},
	month = sep,
	year = {1983},
	pages = {569--576},
	file = {[Wallace.1983] Comment.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/1.CountingPairs/[Wallace.1983] Comment.pdf:application/pdf}
}

@article{simpson_mammals_1943,
	title = {Mammals and the nature of continents},
	volume = {241},
	journal = {American Journal of Science},
	author = {Simpson, G. G.},
	month = jan,
	year = {1943},
	pages = {1--31}
}

@article{dice_measures_1945,
	title = {Measures of the {Amount} of {Ecologic} {Association} {Between} {Species}},
	volume = {26},
	issn = {00129658},
	language = {en},
	number = {3},
	journal = {Ecology},
	author = {Dice, Lee R.},
	month = jul,
	year = {1945},
	pages = {297--302},
	file = {[Dice.1945] Measures of the Amount of Ecologic Association Between Species.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/1.CountingPairs/[Dice.1945] Measures of the Amount of Ecologic Association Between Species.pdf:application/pdf}
}

@book{segaran_programming_2007,
	address = {Beijing ; Sebastapol [CA]},
	edition = {1st ed},
	title = {Programming collective intelligence: building smart web 2.0 applications},
	isbn = {978-0-596-52932-1},
	shorttitle = {Programming collective intelligence},
	publisher = {O'Reilly},
	author = {Segaran, Toby},
	year = {2007},
	note = {OCLC: ocn166886837},
	keywords = {Information technology, Internet programming, Social aspects}
}

@article{tversky_features_1977,
	title = {Features of similarity.},
	volume = {84},
	issn = {0033-295X},
	language = {en},
	number = {4},
	journal = {Psychological Review},
	author = {Tversky, Amos},
	year = {1977},
	pages = {327--352},
	file = {[Tversky.1977] Features of similarity.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/1.CountingPairs/[Tversky.1977] Features of similarity.pdf:application/pdf}
}

@article{danon_comparing_2005,
	title = {Comparing community structure identification},
	volume = {2005},
	issn = {1742-5468},
	number = {09},
	journal = {Journal of Statistical Mechanics: Theory and Experiment},
	author = {Danon, Leon and Díaz-Guilera, Albert and Duch, Jordi and Arenas, Alex},
	month = sep,
	year = {2005},
	pages = {P09008--P09008},
	file = {[Danon.2005] Comparing community structure identification.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/3.InfoTheory/[Danon.2005] Comparing community structure identification.pdf:application/pdf}
}

@article{lancichinetti_detecting_2009,
	title = {Detecting the overlapping and hierarchical community structure in complex networks},
	volume = {11},
	issn = {1367-2630},
	number = {3},
	journal = {New Journal of Physics},
	author = {Lancichinetti, Andrea and Fortunato, Santo and Kertész, János},
	month = mar,
	year = {2009},
	pages = {033015},
	file = {[Lancichinetti.2009] Detecting the overlapping and hierarchical community structure in complex.pdf:/home/kisung/Dropbox/V3. Statistics/P6. ML/S1. Comparing Clusterings/3.InfoTheory/[Lancichinetti.2009] Detecting the overlapping and hierarchical community structure in complex.pdf:application/pdf}
}
