A data mining strategy for inductive data clustering: a synergy between self-organising neural networks and K-means clustering techniques

Self-organising neural networks have a natural propensity to cluster well-defined data into visually distinct clusters, which can then be easily interpretable by data analysts. However, there are situations when the clustering output of the self-organising network does not render distinct clusters. In this paper, we present a technique to automate the data mining task of data clustering, i.e. to automate cluster identification/demarcation by drawing upon a synergy between the self-organising neural networks and statistical data clustering techniques. The implied hybrid of diverse data clustering techniques provides an improved strategy to (a) discover hidden similarities between data items; (b) group similar data items into distinct and well-defined clusters - i.e. with explicit boundaries between different clusters and defined cluster membership characteristics; and (c) visualise the emergent data clusters in a 2D and 3D manner. Our proposed solution is implemented in terms of a data clustering workbench (DCW) - an all-encompassing (exploratory) data mining application.