Algorithms for clustering data
Algorithms for clustering data
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Introduction to data structures and algorithms related to information retrieval
Information retrieval
ACM Computing Surveys (CSUR)
Concept decompositions for large sparse text data using clustering
Machine Learning
A clustering strategy based on a formalism of the reproductive process in natural systems
SIGIR '79 Proceedings of the 2nd annual international ACM SIGIR conference on Information storage and retrieval: information implications into the eighties
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning
Artificial Neural Networks and Statistical Pattern Recognition
Artificial Neural Networks and Statistical Pattern Recognition
Non-negative matrix factorization based methods for object recognition
Pattern Recognition Letters
Nonnegative features of spectro-temporal sounds for classification
Pattern Recognition Letters
A Unifying Approach to Hard and Probabilistic Clustering
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
The Journal of Machine Learning Research
Journal of VLSI Signal Processing Systems
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
A Branch and Bound Clustering Algorithm
IEEE Transactions on Computers
Nonnegative matrix factorization with quadratic programming
Neurocomputing
Regularized Alternating Least Squares Algorithms for Non-negative Matrix/Tensor Factorization
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Binary Matrix Factorization with Applications
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Detect and track latent factors with online nonnegative matrix factorization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Extended SMART algorithms for non-negative matrix factorization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
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Recently, a considerable growth of interest in using Nonnegative Matrix Factorization (NMF) for pattern classification and data clustering has been observed. For nonnegative data (observations, data items, feature vectors) many problems of partitional clustering can be modeled in terms of a matrix factorization into two groups of vectors: the nonnegative centroid vectors and the binary vectors of cluster indicators. Hence our data partitional clustering problem boils down to a semi-binary NMF problem. Usually, NMF problems are solved with an alternating minimization of a given cost function with multiplicative algorithms. Since our NMF problem has a particular characteristics, we apply a different algorithm for updating the estimated factors than commonly-used, i.e. a binary update with simulated annealing steering. As a result, our algorithm outperforms some well-known algorithms for partitional clustering.