Cartification : from similarities to itemset frequencies
Faculty of Sciences. Mathematics and Computer Science

article

2012
2012

Mathematics

Computer. Automation

Lecture notes in computer science

7278(2012)
, p. 4-

0302-9743

E

English (eng)

University of Antwerp

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.