Algorithms for clustering data
Algorithms for clustering data
A deterministic annealing approach to clustering
Pattern Recognition Letters
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization and detection of noise in clustering
Pattern Recognition Letters
On a class of fuzzy classification maximum likelihood procedures
Fuzzy Sets and Systems
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: concepts and techniques
Data mining: concepts and techniques
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Cluster validity methods: part I
ACM SIGMOD Record
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Journal of Global Optimization
Clustering validity checking methods: part II
ACM SIGMOD Record
Fuzzy Clustering Using A Compensated Fuzzy Hopfield Network
Neural Processing Letters
Detection and Separation of Ring-Shaped Clusters Using Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification
Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Kernel k-means: spectral clustering and normalized cuts
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
HARP: A Practical Projected Clustering Algorithm
IEEE Transactions on Knowledge and Data Engineering
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
General C-Means Clustering Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generic Framework for Efficient Subspace Clustering of High-Dimensional Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Some refinements of rough k-means clustering
Pattern Recognition
Locally adaptive metrics for clustering high dimensional data
Data Mining and Knowledge Discovery
On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
IEEE Transactions on Knowledge and Data Engineering
Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques
Journal of Classification
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A survey of kernel and spectral methods for clustering
Pattern Recognition
A tutorial on spectral clustering
Statistics and Computing
A convergence theorem for the fuzzy subspace clustering (FSC) algorithm
Pattern Recognition
Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters
IEEE Transactions on Knowledge and Data Engineering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Density-weighted fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
Shadowed c-means: Integrating fuzzy and rough clustering
Pattern Recognition
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
A robust fuzzy local information C-means clustering algorithm
IEEE Transactions on Image Processing
A time-efficient pattern reduction algorithm for k-means clustering
Information Sciences: an International Journal
Fast modified global k-means algorithm for incremental cluster construction
Pattern Recognition
A new algorithm for initial cluster centers in k-means algorithm
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving k-modes algorithm considering frequencies of attribute values in mode
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Subspace clustering of text documents with feature weighting k-means algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A new and efficient k-medoid algorithm for spatial clustering
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Shadowed sets: representing and processing fuzzy sets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust clustering by pruning outliers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough–Fuzzy Collaborative Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Fuzzy shell clustering algorithms in image processing: fuzzy C-rectangular and 2-rectangular shells
IEEE Transactions on Fuzzy Systems
Generalized fuzzy c-means clustering strategies using Lp norm distances
IEEE Transactions on Fuzzy Systems
Fast accurate fuzzy clustering through data reduction
IEEE Transactions on Fuzzy Systems
Improved possibilistic C-means clustering algorithms
IEEE Transactions on Fuzzy Systems
Optimality test for generalized FCM and its application to parameter selection
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A Robust Automatic Merging Possibilistic Clustering Method
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Least squares quantization in PCM
IEEE Transactions on Information Theory
The fuzzy c spherical shells algorithm: A new approach
IEEE Transactions on Neural Networks
Clustering high dimensional data
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Partitional clustering is an important part of cluster analysis. Cluster analysis can be considered as one of the the most important approaches to unsupervised learning. The goal of clustering is to find clusters from unlabeled data, which means that data belonging to the same cluster are as similar as possible, whereas data belonging to different clusters are as dissimilar as possible. Partitional clustering is categorized as a prototype-based model, i.e., each cluster can be represented by a prototype, leading to a concise description of the original data set. According to different definitions of prototypes, such as data point, hyperplane, and hypersphere, the clustering methods can be categorized into different types of clustering algorithms with various prototypes. Besides organizing these partitional clustering methods into such a unified framework, relations between some commonly used nonpartitional clustering methods and partitional clustering methods are also discussed here. We give a brief overview of clustering, summarize well-known partitional clustering methods, and discuss the major challenges and key issues of these methods. Simple numerical experiments using toy data sets are carried out to enhance the description of various clustering methods. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.