Abstract
This paper studies clustering of objects on the basis of set-valued attributes, so that objects in the same cluster share as many attribute values as possible. Our primary application is clustering of objects participating in a many-to-many relationship. Since the precise optimum is computationally too hard to find, a relatively simple and fast heuristic is developed. The sets of attribute values are represented by non-unique, fixed-size signatures, which constitute the basis for clustering. Objects to be clustered are stored on the leaf pages of a binary tree, where each internal node contains a pair of signatures directing the search for a suitable leaf. The core of the method is a page splitting algorithm, which tries to combine two endeavours: enhance clustering and keep the tree balanced. In a random case, there is little chance for beneficial clustering. However, the dependencies and correlations between real-life objects enable us to achieve notable increase of performance.
This work was supported by the Academy of Finland
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© 1997 Springer-Verlag Berlin Heidelberg
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Teuhola, J. (1997). Heuristic clustering of database objects according to multi-valued attributes. In: Hameurlain, A., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 1997. Lecture Notes in Computer Science, vol 1308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0022028
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DOI: https://doi.org/10.1007/BFb0022028
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