Multi-agent data mining with negotiation : a study in multi-agent based clustering

Multi-Agent Data Mining (MADM) seeks to harness the general advantages offered by Multi-Agent System (MAS) with respect to the domain of data mining. The research described in this thesis is concerned with Multi-Agent Based Clustering (MABC), thus MADM to support clustering. To investigate the use of MAS technology with respect to data mining, and specifically data clustering, two approaches are proposed in this thesis. The first approach is a multi-agent based approach to clustering using a generic MADM framework whereby a collection of agents with different capabilities are allowed to collaborate to produce a “best” set of clusters. The framework supports three clustering paradigms: K-means, K-NN and divisive hierarchical clustering. A number of experiments were conducted using benchmark UCI data sets and designed to demonstrate that the proposed MADM approach can identify a best set of clusters using the following clustering metrics: F-measure, Within Group Average Distance (WGAD) and Between Group Average Distance (BGAD). The results demonstrated that the MADM framework could successfully be used to find a best cluster configuration. The second approach is an extension of the proposed initial MADM framework whereby a “best” cluster configuration could be found using cooperation and negotiation among agents. The novel feature of the extended framework is that it adopts a two-phase approach to clustering. Phase one is similar to the established centralised clustering approach (except that it is conducted in a decentralised manner). Phase two comprises a negotiation phase where agents “swap” unwanted records so as to improve a cluster configuration. A set of performatives is proposed as part of a negotiation protocol to facilitate intra-agent negotiation. It is this negotiation capability which is the central contribution of the work described in this thesis. An extensive evaluation of the extended framework was conducted using: (i) benchmark UCI data sets and (ii) a welfare benefits data set that provides an exemplar application. Evaluation of the framework clearly demonstrates that, in the majority of cases, this negotiation phase serves to produce a better cluster configuration (in terms of cohesion and separation) than that produced using a simple centralised approach.