A new unsupervised clustering method based on outlier information

Traditional clustering algorithms such as CURE and ROCK require the user to provide the number of final clusters k, and outliers are treated as "noise" in the clustering process. By regarding outliers as valuable information, this paper takes a new perspective and complements with classica...

Full description

Saved in:
Bibliographic Details
Main Authors: Tian-Yang Lv, Zheng-Xuan Wang, Wan-Li Zuo
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Clustering methods
Computer science
Content based retrieval
Educational institutions
Image databases
Image retrieval
Machine learning algorithms
Robustness
Unsupervised learning
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Traditional clustering algorithms such as CURE and ROCK require the user to provide the number of final clusters k, and outliers are treated as "noise" in the clustering process. By regarding outliers as valuable information, this paper takes a new perspective and complements with classical approaches. The proposed method integrates outlier identification with cluster number determination, leading to a more robust and truly unsupervised learning paradigm. To demonstrate its feasibility, two improved clustering algorithms CURED and As-ROCK are constructed based on CURE and ROCK. Empirical results demonstrate that these two novel algorithms not only can stop automatically, but also gain much in performance.
DOI:10.1109/ICMLC.2004.1382018