University of Anbar
The analysis of high dimensional data comes with many intrinsic challenges. In particular, cluster structures become increasingly hard to detect when the data includes dimensions irrelevant to the individual clusters. With increasing dimensionality, distances between pairs of objects become very similar, and hence, meaningless for knowledge discovery. In this paper, the authors propose Cartification, a new transformation to circumvent this problem. They transform each object into an itemset, which represents the neighborhood of the object.