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By Pascal Poncelet; Maguelonne Teisseire; Florent Masseglia

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It uses a top-down approach for finding the frequent patterns that satisfy the constraints, where many pruning ideas are incorporated in this algorithm such as in some cases pruning the COFI trees themselves, if the algorithm detects early that all what will be generated from this COFI-tree violates one type of the constraints tested. , 2005) also uses the concept of COFI tree. However, it extends this concept by applying a fast traversal approach called leap. In this approach it generates the maximal patterns first.

We focus on an incremental clustering algorithm that can be applied to nominal data, that is, to data whose attributes have no particular natural ordering. In general clustering, objects to be clustered are represented as points in an n-dimensional space Rn and standard distances, such as the Euclidean distance is used to evaluate similarity between objects. ), no such natural representation of objects is possible, which leaves only the Hamming distance as a dissimilarity measure, a poor choice for discriminating among multivalued attributes of objects.

Journal of Biomedical Informatics, 285-292. Can, F. (1993). Incremental clustering for dynamic information processing. ACM Transactions for Information Systems, 11, 143-164. , Fox, E. , Snavely, C. D. & France, R. K. (1995). Incremental clustering for very large document databases: Initial {MARIAN} experience. Inf. , 84, 101-114. , & Grossberg, S. (1990). Art3: Hierachical search using chemical transmitters in self-organizing pattern recognition architectures. Neural Networks, 3, 129-152. , & López de Mántaras, R.

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