Multi-Agent Based Clustering: Towards Generic Multi-Agent Data Mining

Abstract

Abstract.A framework for Multi Agent Data Mining (MADM) is described. The framework comprises a collection of agents cooperating to address given data mining tasks. The fundamental concept underpinning the framework is that it should support generic data mining. The vision is that of a system that grows in an organic manner. The central issue to facilitating this growth is the communication medium required to support agent interaction. This issue is partly addressed by the nature of the proposed architecture and partly through an extendable ontology; both are described. The advantages offered by the framework are illustrated in this paper by considering a clustering application. The motivation for the latter is that no "best" clustering algorithm has been identified, and consequently an agent-based approach can be adopted to identify "best" clusters. The application serves to demonstrates the full potential of MADM.

Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

“…These findings are currently being incorporated into a MAS framework [6,7]. The techniques investigated sofar, and reported here, do not serve to find the best results in all cases and further investigation is therefore required, however the authors are greatly encouraged by the result reported in this paper.…”

“…The process was incorporated into a MAC framework founded on earlier work by the authors and reported in [6,7]. An issue with K-means is that the initial points (records/objects) used to define the initial centroids of the clusters are randomly selected.…”

“…The need to be able to automatically identify best parameters with respect to a notion of a "best" cluster configuration is of particular relevance in the context of Multi Agent Clustering (MAC) as suggested in [6,7]. The view of clustering presented in [6] is that of a "anarchic" collection of agents: some equipped with algorithms to conduct clustering operations or to validate the output from clustering algorithms, some holding data, and others performing house keeping and management tasks.…”

A framework for Multi-Agent Based Clustering

“…This offers advantages where it is not easy or not possible for data to be first collated; for example, because of security and privacy preserving issues or because of technical issues. MADM also supports the creation of frameworks that can be allowed to grow in an almost anarchic manner, additional agents can be easily incorporated into such frameworks as long as they comply with whatever protocols have been specified [2,11]. However, the true power of agents is their ability to behave in an autonomous manner and interact (negotiate) in order to provide a solution to a problem.…”

“…Clustering results are available at anytime and are continuously revised to achieve the global clustering. Previous work by the authors [11] investigated a MABC mechanism founded on the framework presented in [2], however this could be more accurately be described as a distributed clustering system founded on an agent architecture so that the benefits of this architecture (ready to use communication channels, etc.) could be adopted.…”