Faculty Advisor

Christopher, Peter R.

Faculty Advisor

Sarkozy, Gabor N

Abstract

Data clustering is an immensely powerful tool. The analysis of big data has led to many clustering techniques. Among these techniques is Regularity Clustering, a new technique based on Abel Prize winner Endre Szemerédi's Regularity Lemma. Regularity Clustering has been shown to outperform industry standard clustering techniques in many circumstances. In this report we present new methods of executing Regularity Clustering. Among these methods one, which we call the most recurring construction method, outperforms the standard Regularity Clustering method by a significant margin. We also present empirical evidence indicating when Regularity Clustering performs well.

Publisher

Worcester Polytechnic Institute

Date Accepted

March 2014

Major

Mathematical Sciences

Project Type

Major Qualifying Project

Accessibility

Unrestricted

Advisor Department

Mathematical Sciences

Advisor Department

Computer Science

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