Variable Weighting in Fuzzy k-Means Clustering to Determine the Number of Clusters
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Joshua Zhexue Huang | Zongwei Luo | Waseem Shahzad | Imran Khan | J. Huang | Zongwei Luo | W. Shahzad | Imran Khan
[1] Zongwei Luo,et al. Nonnegative Matrix Factorization Based Consensus for Clusterings With a Variable Number of Clusters , 2018, IEEE Access.
[2] Enrique H. Ruspini,et al. A New Approach to Clustering , 1969, Inf. Control..
[3] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[4] Hichem Frigui,et al. Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation. II , 1995, IEEE Trans. Fuzzy Syst..
[5] Yunming Ye,et al. Neighborhood Density Method for Selecting Initial Cluster Centers in K-Means Clustering , 2006, PAKDD.
[6] Nguyen Thanh Tung,et al. Ensemble Clustering of High Dimensional Data with FastMap Projection , 2014, PAKDD Workshops.
[7] Md Abdul Masud,et al. CPLP: An algorithm for tracking the changes of power consumption patterns in load profile data over time , 2018, Inf. Sci..
[8] Xiaojun Chen,et al. Subspace Weighting Co-Clustering of Gene Expression Data , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[9] Ming Zhong,et al. TWCC: Automated Two-way Subspace Weighting Partitional Co-Clustering , 2018, Pattern Recognit..
[10] Michael K. Ng,et al. Automated variable weighting in k-means type clustering , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Joshua Zhexue Huang,et al. Incremental density-based ensemble clustering over evolving data streams , 2016, Neurocomputing.
[12] Lei Xu,et al. A Comparative Study of Several Cluster Number Selection Criteria , 2003, IDEAL.
[13] T. Ferguson. A Bayesian Analysis of Some Nonparametric Problems , 1973 .
[14] Andrew R. Webb,et al. Statistical Pattern Recognition , 1999 .
[15] Adele Cutler,et al. Information Ratios for Validating Mixture Analysis , 1992 .
[16] Athman Bouguettaya,et al. Adaptive Subspace Symbolization for Content-Based Video Detection , 2010, IEEE Transactions on Knowledge and Data Engineering.
[17] Ming Zhong,et al. I-nice: A new approach for identifying the number of clusters and initial cluster centres , 2018, Inf. Sci..
[18] Claudio Carpineto,et al. Consensus Clustering Based on a New Probabilistic Rand Index with Application to Subtopic Retrieval , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Michael J. Laszlo,et al. A genetic algorithm using hyper-quadtrees for low-dimensional k-means clustering , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Michael K. Ng,et al. An optimization algorithm for clustering using weighted dissimilarity measures , 2004, Pattern Recognit..
[21] Shengrui Wang,et al. FCM-Based Model Selection Algorithms for Determining the Number of Clusters , 2004, Pattern Recognit..
[22] C. L. Philip Chen,et al. Cluster number selection for a small set of samples using the Bayesian Ying-Yang model , 2002, IEEE Trans. Neural Networks.
[23] James C. Bezdek,et al. A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] Chong Wang,et al. Online Variational Inference for the Hierarchical Dirichlet Process , 2011, AISTATS.
[25] Shaina Race,et al. Determining the Number of Clusters via Iterative Consensus Clustering , 2014, SDM.
[26] S. Deelers,et al. Enhancing K-Means Algorithm with Initial Cluster Centers Derived from Data Partitioning along the Data Axis with the Highest Variance , 2007 .
[27] Samuel J. Gershman,et al. A Tutorial on Bayesian Nonparametric Models , 2011, 1106.2697.
[28] Joshua Zhexue Huang,et al. Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.
[29] Greg Hamerly,et al. Learning the k in k-means , 2003, NIPS.
[30] Michael I. Jordan,et al. Revisiting k-means: New Algorithms via Bayesian Nonparametrics , 2011, ICML.
[31] Hichem Frigui,et al. Clustering by competitive agglomeration , 1997, Pattern Recognit..
[32] Michael K. Ng,et al. Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters , 2008, IEEE Transactions on Knowledge and Data Engineering.
[33] V. J. Rayward-Smith,et al. Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , 1999 .
[34] Christian Hennig,et al. Recovering the number of clusters in data sets with noise features using feature rescaling factors , 2015, Inf. Sci..
[35] Yee Whye Teh,et al. Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes , 2004, NIPS.
[36] Yunming Ye,et al. TW-k-means: Automated two-level variable weighting clustering algorithm for multiview data , 2013, IEEE Transactions on Knowledge and Data Engineering.
[37] Md Abdul Masud,et al. Segmentation of Factories on Electricity Consumption Behaviors Using Load Profile Data , 2016, IEEE Access.
[38] Kotagiri Ramamohanarao,et al. Automatically Determining the Number of Clusters in Unlabeled Data Sets , 2009, IEEE Transactions on Knowledge and Data Engineering.
[39] Pietro Perona,et al. A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[40] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[41] R. Yager,et al. Approximate Clustering Via the Mountain Method , 1994, IEEE Trans. Syst. Man Cybern. Syst..
[42] Weixiang Liu,et al. An experimental comparison of gene selection by Lasso and Dantzig selector for cancer classification , 2011, Comput. Biol. Medicine.
[43] Jing He,et al. Clustering of Multiple Density Peaks , 2018, PAKDD.
[44] M. Escobar,et al. Bayesian Density Estimation and Inference Using Mixtures , 1995 .