Abstract--Large high dimension datasets are of growing importance in many fields and it is important to be able to visualize them for understanding the results of data mining appro...
Jong Youl Choi, Seung-Hee Bae, Xiaohong Qiu, Geoff...
Abstract. In this paper, we present a new approach to indexing multidimensional data that is particularly suitable for the efficient incremental processing of nearest neighbor quer...
Many scientific applications generate large spatiotemporal datasets. A common way of exploring these datasets is to identify and track regions of interest. Usually these regions ...
Kesheng Wu, Wendy S. Koegler, Jacqueline Chen, Ari...
We focus on clustering gene expression temporal profiles, and propose a novel, simple algorithm that is powerful enough to find an efficient distribution of genes over clusters. We...
High dimensionality remains a significant challenge for document clustering. Recent approaches used frequent itemsets and closed frequent itemsets to reduce dimensionality, and to...