By Reinhold Decker
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The preliminary experiments with real symbolic data sets, done by the author, also conﬁrm the quality of these indexes in the symbolic data case. Cluster Quality Indexes for Symbolic Classiﬁcation – An Examination 37 The results can be explained by the fact that Hubert and Levine and the Baker and Hubert indexes are based on distance matrices and for them, limitations of symbolic methods, described in section 2, do not exist. Table 3. Comparison of cluster quality indexes for symbolic data – Dynamical clustering.
The situation diﬀers in case of symbolic data. There are no hints in literature which indexes are most appropriate for that kind of data. This paper describes cluster quality indexes that can be used in this case. In the ﬁrst part clustering methods that can be used for symbolic data and methods designed exclusively for this kind of data are described. The second part presents main groups of cluster quality indexes along with examples of indexes from each group (due to the lack of space only the most frequently used indexes are described).
J. J. (1971) Clustering Methods Based on Likelihood Ratio Criteria. Biometrics, 27, 387-397. VERDE, R. (2004): Clustering Methods in Symbolic Data Analysis. In: D. Banks et al. ): Classiﬁcation, Clustering and Data Mining Applications, Springer, Berlin, 299-318. , DIMITRIADOU, A. and DOLNICAR, S. (1999): An Examination Of Indexes For Determining The Number Of Clusters In Binary Data Sets. htm#29. T. pt Abstract. This paper describes a new approach to semi-supervised model-based clustering. The problem is formulated as penalized logistic regression, where the labels are only indirectly observed (via the component densities).