Cart (Loading....) | Create Account
Close category search window

An inheritable clustering algorithm suited for parameter changing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Fei Li ; Lab. of Intelligent Inf. Process., NanKai Univ., Tianjin, China ; Shang Liu ; Zhi-Tong Dou ; Ya-Lou Huang

DBSCAN is a classic density based algorithm and it clusters the data set according to the user input parameters. This work investigates how to inherit the mining results of last time when parameters change. A new incremental clustering algorithm IPC-DBSCAN is proposed, which gets the same result as that of rerunning DBSCAN yet high efficiency is achieved. Theoretical analysis and experimental results show that the proposed method reduces search space greatly and has novel efficiency. By interaction, IPC-DBSCAN gets the most satisfying result quickly and especially suits large volume data set.

Published in:

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on  (Volume:2 )

Date of Conference:

26-29 Aug. 2004

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.