Search

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. [ABSTRACT FROM AUTHOR]

The article introduces a concept and design of decision trees based on information granules, multivariable entities characterized by high homogeneity. The article shows how the tree is grown depending on some additional node expansion criteria such as cardinality at a given node and a level of structural dependencies of data existing there. A series of experiments is reported using both synthetic and machine learning data sets. The C-decision trees are classification constructs that are built on a basis of information granules, fuzzy clusters.

Abstract: Several fuzzy c-means based clustering techniques have been developed to tackle many problems in a number of areas such as pattern recognition, image analysis, communication, data mining. Among all, a common use of such a class of clustering algorithms is in the training of radial basis function neural networks (RBFNNs). In this paper, we describe a novel approach to fuzzy clustering, which organizes the data in clusters on the basis of the input data and a ‘prototype’ regression function built, in the output space, as a summation of a number of linear local regression models. This methodology is shown to be effective in the training of RBFNNs leading to improved performance with respect to other clustering algorithms. [Copyright &y& Elsevier]