Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Collaborative fuzzy clustering
Pattern Recognition Letters
Design and evaluation of a multi-agent collaborative Web mining system
Decision Support Systems - Web retrieval and mining
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
Simulating Internet-based collaboration: A cost-benefit case study using a multi-agent model
Decision Support Systems
Innovations in multi-agent systems
Journal of Network and Computer Applications
A multiview approach for intelligent data analysis based on data operators
Information Sciences: an International Journal
Mobile-agent-based collaborative sensor fusion
Information Fusion
Agent-based distributed architecture for mobile robot control
Engineering Applications of Artificial Intelligence
Inference in distributed data clustering
Engineering Applications of Artificial Intelligence
Editorial Recent Advances in Cognitive Informatics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
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Multiagent systems are inherently associated with their distributivity, which enforces a great deal of communication mechanisms. To effectively arrive at meaningful solutions in a vast array of problem-solving tasks, it becomes imperative to establish a sound machinery of reconciling findings which might form partial solutions to an overall problem. In this paper, we focus on a broad category of problems of collaborative data analysis realized by a collection of agents having access to their individual data and exchanging findings through their collaboration activities. Such problems of data analysis arise in the context of building a global view at a certain phenomenon (process) by viewing it from different perspectives (and thus engaging various collections of attributes by various agents). Our goal is to develop some interaction between the agents so that they could form an overall perspective, where the knowledge available locally is shared and reconciled. The underlying format of knowledge built by the agents is that of information granules and fuzzy sets in particular. We develop a comprehensive optimization scheme and discuss its two-phase nature in which the communication phase of the granular findings intertwines with the local optimization being realized by the agents at the level of the individual datasite and exploits the evidence collected from other sites. We show how the mechanism of fuzzy granulation realized in the form of a well-known fuzzy c-means (FCM) clustering can be augmented to support collaborative activities required by the agents. For this purpose, we introduce augmented versions of the original objective function used in the FCM and derive algorithmic details. We also discuss an issue of optimizing the strength of collaborative linkages, so that the reconciled findings attain the highest level of consistency (agreement). The presented experimental studies include some synthetic data and selected data sets coming from the Machine Learning repository.