Fuzzy mathematical techniques with applications
Fuzzy mathematical techniques with applications
Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to the analysis of algorithms
An introduction to the analysis of algorithms
Application of the least trimmed squares technique to prototype-based clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
On finding the number of clusters
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Distributed clustering using collective principal component analysis
Knowledge and Information Systems
Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification
Annals of Mathematics and Artificial Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
The Journal of Machine Learning Research
A divisive information theoretic feature clustering algorithm for text classification
The Journal of Machine Learning Research
Effective and Efficient Distributed Model-Based Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Unsupervised possibilistic clustering
Pattern Recognition
A method for initialising the K-means clustering algorithm using kd-trees
Pattern Recognition Letters
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A Scalable Framework For Segmenting Magnetic Resonance Images
Journal of Signal Processing Systems
Parallel Clustering Algorithm for Large Data Sets with Applications in Bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computational Statistics & Data Analysis
Clustering of time series data-a survey
Pattern Recognition
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Relative clustering validity criteria: A comparative overview
Statistical Analysis and Data Mining
Analysis of parameter selections for fuzzy c-means
Pattern Recognition
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Some new indexes of cluster validity
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
A Cluster Validity Measure With Outlier Detection for Support Vector Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Comments on “A possibilistic approach to clustering”
IEEE Transactions on Fuzzy Systems
Correction to "On Cluster Validity for the Fuzzy c-Means Model" [Correspondence]
IEEE Transactions on Fuzzy Systems
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Will the real iris data please stand up?
IEEE Transactions on Fuzzy Systems
Low-complexity fuzzy relational clustering algorithms for Web mining
IEEE Transactions on Fuzzy Systems
Fast accurate fuzzy clustering through data reduction
IEEE Transactions on Fuzzy Systems
Fuzzy Clustering for Data Time Arrays With Inlier and Outlier Time Trajectories
IEEE Transactions on Fuzzy Systems
Visual Assessment of Clustering Tendency for Rectangular Dissimilarity Matrices
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
The fuzzy c spherical shells algorithm: A new approach
IEEE Transactions on Neural Networks
A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Vague C-means clustering algorithm
Pattern Recognition Letters
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Clustering is typically applied for data exploration when there are no or very few labeled data available. The goal is to find groups or clusters of like data. The clusters will be of interest to the person applying the algorithm. An objective function-based clustering algorithm tries to minimize (or maximize) a function such that the clusters that are obtained when the minimum/maximum is reached are homogeneous. One needs to choose a good set of features and the appropriate number of clusters to generate a good partition of the data into maximally homogeneous groups. Objective functions for clustering are introduced. Clustering algorithms generated from the given objective functions are shown, with a number of examples of widely used approaches discussed. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.