Towards supporting expert evaluation of clustering results using a data mining process model

Clustering is a popular non-directed learning data mining technique for partitioning a dataset into a set of clusters (i.e. a segmentation). Although there are many clustering algorithms, none is superior on all datasets, and so it is never clear which algorithm and which parameter settings are the most appropriate for a given dataset. This suggests that an appropriate approach to clustering should involve the application of multiple clustering algorithms with different parameter settings and a non-taxing approach for comparing the various segmentations that would be generated by these algorithms. In this paper we are concerned with the situation where a domain expert has to evaluate several segmentations in order to determine the most appropriate segmentation (set of clusters) based on his/her specified objective(s). We illustrate how a data mining process model could be applied to address this problem.