RUI: Novel Enhancements to the K-Means Clustering Algorithm

Louisiana State University Shreveport, Shreveport LA

Investigators

Abstract

Clustering is a crucial component of exploratory data analysis. This project aims to develop novel algorithms that address various shortcomings of k-means, the most widely used clustering algorithm. Specific objectives include (a) the development of initialization methods to address the sensitivity of k-means to the initial cluster centers; (b) the investigation of alternative distance measures to address the sensitivity of k-means to outliers; and (c) the development of practical acceleration methods. Innovations developed during this project should be readily applicable to a wide range of clustering algorithms. For example, initialization is crucial for most clustering algorithms. Furthermore, an effective initialization method can be used independently of k-means as a standalone clustering algorithm. K-means is often used as a subroutine in other learning algorithms. Therefore, development of acceleration methods for k-means is of great practical interest. The project is expected to make broader impacts on several fronts. At the international level, we intend to make significant contributions to the data mining literature by publishing in top-ranked journals. At the national level, we aim to enhance the competitiveness of the US by seeding the next generation of scientists. At the regional level, we hope to improve the quality of education in an EPSCoR state and contribute to the development of a diverse and skilled workforce. At the institutional level, we intend to improve the research environment and the curriculum of our Computer Science program. Finally, at the individual level, we hope to increase the participation of students from underrepresented groups in research and equip them with valuable skills including self-confidence, independent thinking/problem solving, and effective communication.