Communications of the ACM - Special issue on parallelism
ACM Computing Surveys (CSUR)
Generality-Based Conceptual Clustering with Probabilistic Concepts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Hierarchical Distance-Based Conceptual Clustering
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
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In this work we analyse the relationship between distance and generalisation operators for real numbers, nominal data and tuples in the context of hierarchical distance-based conceptual clustering (HDCC). HDCC is a general approach to conceptual clustering that extends the traditional algorithm for hierarchical clustering by producing conceptual generalisations of the discovered clusters. This makes it possible to combine the flexibility of changing distances for several clustering problems and the advantage of having concepts which are crucial for tasks as summarisation and descriptive data mining in general. In this work we propose a set of generalisation operators and distances for the data types mentioned before and we analyse the properties by them satisfied on the basis of three different levels of agreement between the clustering hierarchy obtained from the linkage distance and the hierarchy obtained by using generalisation operators.