Package conclust. August 15, 2016
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1 Type Package Title Pairwise Constraints Clustering Version 1.1 Date Author Tran Khanh Hiep, Nguyen Minh Duc Package conclust August 15, 2016 Maintainer Tran Khanh Hiep There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of mustlink and cannot-link constraints as input and produce a clustering as output. License GPL-3 NeedsCompilation no Repository CRAN Date/Publication :16:21 R topics documented: conclust-package ccls ckmeans lcvqe mpckm Index 8 conclust-package Pairwise Constraints Clustering There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output. 1
2 2 conclust-package The DESCRIPTION file: Package: conclust Type: Package Title: Pairwise Constraints Clustering Version: 1.1 Date: Author: Tran Khanh Hiep, Nguyen Minh Duc Maintainer: Tran Khanh Hiep : There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled da License: GPL-3 Index of help topics: ccls ckmeans conclust-package lcvqe mpckm Pairwise Constrained Clustering by Local Search COP K-means algorithm Pairwise Constraints Clustering LCVQE algorithm MPC K-means algorithm There are 4 main functions in this package: ckmeans(), lcvqe(), mpckm() and ccls(). They take an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output. Tran Khanh Hiep, Nguyen Minh Duc Maintainer: Tran Khanh Hiep <hieptkse03059@fpt.edu.vn> Wagstaff, Cardie, Rogers, Schrodl (2001), Constrained K-means Clustering with Background Knowledge Bilenko, Basu, Mooney (2004), Integrating Constraints and Metric Learning in Semi-Supervised Clustering Dan Pelleg, Dorit Baras (2007), K-means with large and noisy constraint sets Wagstaff, Cardie, Rogers, Schrodl (2001), Constrained K-means Clustering with Background Knowledge Bilenko, Basu, Mooney (2004), Integrating Constraints and Metric Learning in Semi-Supervised Clustering Dan Pelleg, Dorit Baras (2007), K-means with large and noisy constraint sets
3 ccls 3 = ckmeans(data, k, mustlink, cantlink) = mpckm(data, k, mustlink, cantlink) = lcvqe(data, k, mustlink, cantlink) = ccls(data, k, mustlink, cantlink) ccls Pairwise Constrained Clustering by Local Search Usage This function takes an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output. ccls(data, k = -1, mustlink, cantlink, maxiter = 1, tabuiter = 100, tabulength = 20) Arguments data k mustlink cantlink maxiter tabuiter tabulength The unlabeled dataset. Number of clusters. A list of must-link constraints A list of cannot-link constraints Number of iteration Number of iteration in Tabu search The number of elements in the Tabu list This algorithm minimizes the clustering cost function using Tabu search. Value A vector that represents the labels (clusters) of the data points Note This is the first algorithm for pairwise constrained clustering by local search. Tran Khanh Hiep Nguyen Minh Duc
4 4 ckmeans Tran Khanh Hiep, Nguyen Minh Duc, Bui Quoc Trung (2016), Pairwise Constrained Clustering by Local Search. Tran Khanh Hiep, Nguyen Minh Duc, Bui Quoc Trung (2016), Pairwise Constrained Clustering by Local Search. = ckmeans(data, k, mustlink, cantlink) ckmeans COP K-means algorithm This function takes an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output. Usage ckmeans(data, k, mustlink, cantlink, maxiter = 100) Arguments data k mustlink cantlink maxiter The unlabeled dataset. Number of clusters. A list of must-link constraints A list of cannot-link constraints Number of iteration This algorithm produces a clustering that satisfies all given constraints. Value A vector that represents the labels (clusters) of the data points
5 lcvqe 5 Note The constraints should be consistent in order for the algorithm to work. Tran Khanh Hiep Nguyen Minh Duc Wagstaff, Cardie, Rogers, Schrodl (2001), Constrained K-means Clustering with Background Knowledge Wagstaff, Cardie, Rogers, Schrodl (2001), Constrained K-means Clustering with Background Knowledge = ckmeans(data, k, mustlink, cantlink) lcvqe LCVQE algorithm This function takes an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output. Usage lcvqe(data, k, mustlink, cantlink, maxiter = 10) Arguments data k mustlink cantlink maxiter The unlabeled dataset. Number of clusters. A list of must-link constraints A list of cannot-link constraints Number of iteration
6 6 mpckm This algorithm finds a clustering that satisfies as many constraints as possible Value A vector that represents the labels (clusters) of the data points Note This algorithm can handle noisy constraints. Tran Khanh Hiep Nguyen Minh Duc Dan Pelleg, Dorit Baras (2007), K-means with large and noisy constraint sets Dan Pelleg, Dorit Baras (2007), K-means with large and noisy constraint sets = lcvqe(data, k, mustlink, cantlink) mpckm MPC K-means algorithm This function takes an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output. Usage mpckm(data, k, mustlink, cantlink, maxiter = 10)
7 mpckm 7 Arguments data k mustlink cantlink maxiter The unlabeled dataset. Number of clusters. A list of must-link constraints A list of cannot-link constraints Number of iteration Value Note This algorithm finds a clustering that satisfies as many constraints as possible A vector that represents the labels (clusters) of the data points This is one of the best algorithm for clustering with constraints. Tran Khanh Hiep Nguyen Minh Duc Bilenko, Basu, Mooney (2004), Integrating Constraints and Metric Learning in Semi-Supervised Clustering Bilenko, Basu, Mooney (2004), Integrating Constraints and Metric Learning in Semi-Supervised Clustering = mpckm(data, k, mustlink, cantlink)
8 Index Topic Tabu search Topic ccls Topic clustering ckmeans, 4 lcvqe, 5 mpckm, 6 Topic constraint ckmeans, 4 lcvqe, 5 mpckm, 6 Topic copkmeans ckmeans, 4 Topic lcvqe lcvqe, 5 Topic local search Topic mpckmeans mpckm, 6 ckmeans, 4 conclust (conclust-package), 1 lcvqe, 5 mpckm, 6 8
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Constrained Locally Weighted Clustering Hao Cheng, Kien A. Hua and Khanh Vu School of Electrical Engineering and Computer Science University of Central Florida Orlando, FL, 386 {haocheng, kienhua, khanh}@eecs.ucf.edu
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Package lhs January 4, 2018 Version 0.16 Date 2017-12-23 Title Latin Hypercube Samples Author [aut, cre] Maintainer Depends R (>= 3.3.0) Suggests RUnit Provides a number of methods
Package SSLASSO. August 28, 2018
Package SSLASSO August 28, 2018 Version 1.2-1 Date 2018-08-28 Title The Spike-and-Slab LASSO Author Veronika Rockova [aut,cre], Gemma Moran [aut] Maintainer Gemma Moran Description
Package ECctmc. May 1, 2018
Type Package Package ECctmc May 1, 2018 Title Simulation from Endpoint-Conditioned Continuous Time Markov Chains Version 0.2.5 Date 2018-04-30 URL https://github.com/fintzij/ecctmc BugReports https://github.com/fintzij/ecctmc/issues
Package CvM2SL2Test. February 15, 2013
Package CvM2SL2Test February 15, 2013 Version 2.0-1 Date 2012-01-02 Title Cramer-von Mises Two Sample Tests Author Yuanhui Xiao Maintainer Yuanhui Xiao Depends Suggests