.png){kind=icon}
Return
Robust validity index for a modified subtractive clustering algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2014.05.001.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A novel robust validity index is proposed for subtractive clustering (SC) algorithm. Although the SC algorithm is a simple and fast data clustering method with robust properties against outliers and noise; it has two limitations. First, the cluster number generated by the SC algorithm is influenced by a given threshold. Second, the cluster centers obtained by SC are based on data that have the highest potential values but may not be the actual cluster centers. The validity index is a function as a measure of the fitness of a partition for a given data set. To solve the first problem, this study proposes a novel robust validity index that evaluates the fitness of a partition generated by SC algorithm in terms of three properties: compactness, separation and partition index. To solve the second problem, a modified algorithm based on distance relations between data and cluster centers is designed to ascertain the actual centers generated by the SC algorithm. Experiments confirm that the preferences of the proposed index outperform all others. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Partition index
Robust
Subtractive clustering (SC) algorithm
Validity index
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
No organization information available
Cited Papers
ShareSaveExoglucanase‐encoding genes from three Wickerhamomyces anomalus killer strains isolated from olive brine.png)
Yeast
YeastIF0
PREAI
ShareSaveShareSaveShareSave
ShareSaveShareSave
View more