Back to Search

An iterative approach to clustering optimization and robustness.

Title	An iterative approach to clustering optimization and robustness.
Author	Gümüş, H., Korkmaz Özay, E., Arı, İsmail, Çataltepe, Z.
Publication Date:	2012
Publication Place	- IEEE
Subject	Iterative methods, Pattern clustering
Type	Document
Language	Turkish
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-4673-0055-1
Record ID	512928b3-7fec-4fb0-ae8d-644fa64275ab
Library Location	Computer Science
Date	2012
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	Clustering is a common technique, in all areas where information is obtained from the collected data. In this work, three well-known clustering algorithms namely, K-means, Spectral and DBSCAN are investigated in terms of their validity using four clustering validity indexes, Rand, Adjusted Rand, Jaccard, Silhouette. These clustering algorithms are applied on three data sets which have different characteristics. Thus steps have been taken for an automated clustering optimization system.
DOI	10.1109/SIU.2012.6204721

View in source Özyeğin University Özyeğin University - Ottoman library catalog search

Özyeğin University

An iterative approach to clustering optimization and robustness.

Author Gümüş, H., Korkmaz Özay, E., Arı, İsmail, Çataltepe, Z.

Publication Date 2012

Publication Place - IEEE

Subject Iterative methods, Pattern clustering

Type Document

Language Turkish

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-4673-0055-1

Record ID 512928b3-7fec-4fb0-ae8d-644fa64275ab

Library Location Computer Science

Date 2012

Notes Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Sample Text Clustering is a common technique, in all areas where information is obtained from the collected data. In this work, three well-known clustering algorithms namely, K-means, Spectral and DBSCAN are investigated in terms of their validity using four clustering validity indexes, Rand, Adjusted Rand, Jaccard, Silhouette. These clustering algorithms are applied on three data sets which have different characteristics. Thus steps have been taken for an automated clustering optimization system.

DOI 10.1109/SIU.2012.6204721