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06080056 Goethals, Bart Cartification: from similarities to itemset frequencies. Domenach, Florent (ed.) et al., Formal concept analysis. 10th international conference, ICFCA 2012, Leuven, Belgium, May 7--10, 2012. Proceedings. Berlin: Springer (ISBN 978-3-642-29891-2/pbk). Lecture Notes in Computer Science 7278. Lecture Notes in Artificial Intelligence, 4 (2012). 2012 Berlin: Springer EN

doi:10.1007/978-3-642-29892-9_4

Summary: Suppose we are given a multi-dimensional dataset. For every point in the dataset, we create a transaction, or cart, in which we store the k-nearest neighbors of that point for one of the given dimensions. The resulting collection of carts can then be used to mine frequent itemsets; that is, sets of points that are frequently seen together in some dimensions. Experimentation shows that finding clusters, outliers, cluster centers, or even subspace clustering becomes easy on the cartified dataset using state-of-the-art techniques in mining interesting itemsets.