Algorithms for clustering data
Algorithms for clustering data
Competitive learning algorithms for vector quantization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
ACM Computing Surveys (CSUR)
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Algorithms
Constraint-based clustering in large databases
ICDT '01 Proceedings of the 8th International Conference on Database Theory
A Sublinear Time Approximation Scheme for Clustering in Metric Spaces
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Model-based Clustering with Soft Balancing
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Relationship-Based Clustering and Visualization for High-Dimensional Data Mining
INFORMS Journal on Computing
Diffusion approximation of frequency sensitive competitive learning
IEEE Transactions on Neural Networks
Model-based Clustering with Soft Balancing
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A unified framework for model-based clustering
The Journal of Machine Learning Research
Clustering time series from ARMA models with clipped data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering Time Series with Clipped Data
Machine Learning
Knowledge and Information Systems
Bounding and comparing methods for correlation clustering beyond ILP
ILP '09 Proceedings of the Workshop on Integer Linear Programming for Natural Langauge Processing
ESPClust: an effective skew prevention method for model-based document clustering
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
CECM: Constrained evidential C-means algorithm
Computational Statistics & Data Analysis
MMPClust: a skew prevention algorithm for model-based document clustering
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
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Balanced clustering algorithms can be useful in a varietyof applications and have recently attracted increasing researchinterest. Most recent work, however, addressed onlyhard balancing by constraining each cluster to have equalor a certain minimum number of data objects. This paperprovides a soft balancing strategy built upon a soft mixture-of-models clustering framework. This strategy constrains the sum of posterior probabilities of object membership foreach cluster to be equal and thus balances the expectednumber of data objects in each cluster. We first derive softmodel-based clustering from an information-theoretic viewpointand then show that the proposed balanced clusteringcan be parameterized by a temperature parameter that controlsthe softness of clustering as well as that of balancing.As the temperature decreases, the resulting partitioning becomesmore and more balanced. In the limit, when temperaturebecomes zero, the balancing becomes hard and theactual partitioning becomes perfectly balanced. The effectivenessof the proposed soft balanced clustering algorithmis demonstrated on both synthetic and real text data.