Master Thesis Defense: Rahmah Brnawy

Speaker: Rahmah Brnawy

Supervisor: Dr. N. Shiri

Examining Committee: Drs. G. Grahne, S. P. Mudur,
P. Rigby (Chair)

Title:  An Efficient Technique for Clustering Data With Mixed Attribute Types

Date: Thursday, June 4, 2015

Time: 14:00

Place: EV 3.309

ABSTRACT

Clustering is a technique used to extract useful information and discover patterns from data. Existing clustering techniques have often focused on datasets with attributes that are either numeric or categorical but not both. The problem of clustering mixed numeric and categorical datasets has received increased attention recently, resulted in a number of solution methods. In this research, we study these solutions and propose two clustering algorithms. The first algorithm is Cluc+, which extends and improves an existing algorithm for clustering pure categorical data. Using Cluc+, we then develop a new algorithm, called K-mixed, for clustering data with mixed numeric and categorical attribute types. We experimentally evaluate the performance of these algorithms using real-life benchmark datasets. Our results indicate increased efficiency and accuracy of our proposed solution techniques.