Algorithms for clustering data
Algorithms for clustering data
Information retrieval
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Combining Image Compression and Classification Using Vector Quantization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Collaborative fuzzy clustering
Pattern Recognition Letters
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
A privacy-sensitive approach to distributed clustering
Pattern Recognition Letters - Special issue: Advances in pattern recognition
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Distributed EM algorithms for density estimation and clustering in sensor networks
IEEE Transactions on Signal Processing
IEEE Transactions on Fuzzy Systems
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Metastructural facets of granular computing
International Journal of Knowledge Engineering and Soft Data Paradigms
Generalized external indexes for comparing data partitions with overlapping categories
Pattern Recognition Letters
Collaborative architectures of fuzzy modeling
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
PSO driven collaborative clustering: A clustering algorithm for ubiquitous environments
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
Colour image segmentation using fuzzy clustering techniques and competitive neural network
Applied Soft Computing
New results on a fuzzy granular space
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Adjusting the clustering results referencing an external set
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Granular fuzzy models: a study in knowledge management in fuzzy modeling
International Journal of Approximate Reasoning
Granular Computing and Human-Centricity in Computational Intelligence
International Journal of Software Science and Computational Intelligence
Building granular fuzzy decision support systems
Knowledge-Based Systems
Hi-index | 0.20 |
In this study, we introduce the concept of collaborative fuzzy clustering-a conceptual and algorithmic machinery for the collective discovery of a common structure (relationships) within a finite family of data residing at individual data sites. There are two fundamental features of the proposed optimization environment. First, given existing constraints which prevent individual sites from exchanging detailed numeric data, any communication has to be realized at the level of information granules. The specificity of these granules impacts the effectiveness of ensuing collaborative activities. Second, the fuzzy clustering realized at the level of the individual data site has to constructively consider the findings communicated by other sites and act upon them while running the optimization confined to the particular data site. Adhering to these two general guidelines, we develop a comprehensive optimization scheme and discuss its two-phase character in which the communication phase of the granular findings intertwines with the local optimization being realized at the level of the individual site and exploits the evidence collected from other sites. The proposed augmented form of the objective function is essential in the navigation of the overall optimization that has to be completed on a basis of the data and available information granules. The intensity of collaboration is optimized by choosing a suitable tradeoff between the two components of the objective function. The objective function based clustering used here concerns the well-known Fuzzy C-Means (FCM) algorithm. Experimental studies presented include some synthetic data, selected data sets coming from the machine learning repository and the weather data coming from Environment Canada.