Algorithms for clustering data
Algorithms for clustering data
RACHET: An Efficient Cover-Based Merging of Clustering Hierarchies from Distributed Datasets
Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
Collaborative fuzzy clustering
Pattern Recognition Letters
Data Clustering Using Evidence Accumulation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
Collaborative clustering with background knowledge
Data & Knowledge Engineering
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
Generalized external indexes for comparing data partitions with overlapping categories
Pattern Recognition Letters
Clustering with proximity knowledge and relational knowledge
Pattern Recognition
Adjusting the clustering results referencing an external set
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Collaborative generative topographic mapping
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Building granular fuzzy decision support systems
Knowledge-Based Systems
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In this study, we are concerned with a concept of consensus-driven fuzzy clustering whose objective is to reconcile a structure developed for patterns in some data set with the structural findings already available for other related data sets (where these data sets are reflective of the same phenomenon which has led to the generation of the original patterns). The results of fuzzy clustering are provided in the form of prototypes and fuzzy partition matrices. Given this form of representation of granular results (clusters), we develop a suitable communication scheme using which consensus could be established in an effective manner. Here, we consider proximity matrices induced by the corresponding partition matrices. An overall optimization scheme is presented in detail along with a way of forming a pertinent criterion governing an intensity of collaboration between the data driven- and knowledge oriented hints guiding the process of consensus formation. Several illustrative numeric examples, using both synthetic data and the data coming from publicly available machine learning repositories are also included.