Knowledge Discovery in High Dimensional Data

Title

Knowledge Discovery in High Dimensional Data

 

Abstract

While data clustering has a long history and a large amount of research has been devoted to the development of numerous clustering techniques, significant challenges still remain. One of the most important of them is associated with high data dimensionality. A particular class of clustering algorithms has been very successful in dealing with such datasets, utilising information driven by dimensionality reduction techniques. Projection methods for dimension reduction have enabled the discovery of otherwise unattainable structure in ultra high dimensional data. In this work, we try to deepen our understanding on what can be achieved by this kind of approaches in an attempt to theoretically discover the relationship between true clusters in the data and the distribution of their projection. Based on such findings, we propose a series of new hierarchical divisive clustering algorithms. The proposed algorithms require minimal user-defined parameters and have the desirable feature of being able to provide approximations for the number of clusters present in the data.

 

About the presenter

Sotirios K. Tasoulis is a Post Doctoral Researcher in "Knowledge Discovery in Big Data" within the Complex Systems Computation Group (CoSCo) at HIIT / University of Helsinki. His Ph.D. thesis is entitled "Knowledge Discovery in High Dimensional Data". He holds a bachelor's degree in Mathematics and a Ms.C in Mathematics and Computer Science.