Algorithms for clustering data
Algorithms for clustering data
On finding the number of clusters
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Information Retrieval
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Review of Spectral Graph Theory: by Fan R. K. Chung
ACM SIGACT News
Detection and Separation of Ring-Shaped Clusters Using Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nested Partitions Method for Global Optimization
Operations Research
Nested Partitions Using Texture Segmentation
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization of Speech Recognition by Clustering of Phones
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
Intelligent Partitioning for Feature Selection
INFORMS Journal on Computing
On-line event and topic detection by using the compact sets clustering algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - IBERAMIA '02
Clustering and Embedding Using Commute Times
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Clustering for Software Architecture Recovery
IEEE Transactions on Software Engineering
Hierarchical Clustering Algorithm Based on Granularity
GRC '07 Proceedings of the 2007 IEEE International Conference on Granular Computing
Structuralization of universes
Fuzzy Sets and Systems
ACONS: a new algorithm for clustering documents
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Dynamic hierarchical compact clustering algorithm
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Fast agglomerative clustering using information of k-nearest neighbors
Pattern Recognition
Incremental threshold learning for classifier selection
Neurocomputing
An indication of unification for different clustering approaches
Pattern Recognition
| Hi-index | 0.01 |
Clustering methods are a powerful tool for discovering patterns in a given data set through an organization of data into subsets of objects that share common features. Motivated by the independent use of some different partitions criteria and the theoretical and empirical analysis of some of its properties, in this paper, we introduce an incremental nested partition method which combines these partitions criteria for finding the inner structure of static and dynamic datasets. For this, we proved that there are relationships of nesting between partitions obtained, respectively, from these partition criteria, and besides that the sensitivity when a new object arrives to the dataset is rigorously studied. Our algorithm exploits all of these mathematical properties for obtaining the hierarchy of clusterings. Moreover, we realize a theoretical and experimental comparative study of our method with classical hierarchical clustering methods such as single-link and complete-link and other more recently introduced methods. The experimental results over databases of UCI repository and the AFP and TDT2 news collections show the usefulness and capability of our method to reveal different levels of information hidden in datasets.