Abstract

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Sponsor(s)

Mary K. Finley Missouri Endowment
National Science Foundation (U.S.)

Keywords and Phrases

Adaptive Resonance Theory (ART); Cluster Validation; Clustering; Clustering Algorithm; Proximity; Self-Organizing Feature Map (SOFM)

Library of Congress Subject Headings

Neural networks (Computer science)

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Full Text Link

Share

 
COinS