Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

TOBAE: A Density-based Agglomerative Clustering Algorithm

TOBAE: A Density-based Agglomerative Clustering Algorithm This paper presents a novel density based agglomerative clustering algorithm named TOBAE which is a parameter-less algorithm and automatically filters noise. It finds the appropriate number of clusters while giving a competitive running time. TOBAE works by tracking the cumulative density distribution of the data points on a grid and only requires the original data set as input. The clustering problem is solved by automatically finding the optimal density threshold for the clusters. It is applicable to any N-dimensional data set which makes it highly relevant for real world scenarios. The algorithm outperforms state of the art clustering algorithms by the additional feature of automatic noise filtration around clusters. The concept behind the algorithm is explained using the analogy of puddles (’tobae’), which the algorithm is inspired from. This paper provides a detailed algorithm for TOBAE along with the complexity analysis for both time and space. We show experimental results against known data sets and show how TOBAE competes with the best algorithms in the field while providing its own set of advantages. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

TOBAE: A Density-based Agglomerative Clustering Algorithm

Journal of Classification , Volume 32 (2) – Mar 3, 2015

Loading next page...
 
/lp/springer-journals/tobae-a-density-based-agglomerative-clustering-algorithm-6PQ0v9OHTK

References (46)

Publisher
Springer Journals
Copyright
Copyright © 2015 by Classification Society of North America
Subject
Statistics; Statistical Theory and Methods; Pattern Recognition; Bioinformatics; Signal, Image and Speech Processing; Psychometrics; Marketing
ISSN
0176-4268
eISSN
1432-1343
DOI
10.1007/s00357-015-9166-2
Publisher site
See Article on Publisher Site

Abstract

This paper presents a novel density based agglomerative clustering algorithm named TOBAE which is a parameter-less algorithm and automatically filters noise. It finds the appropriate number of clusters while giving a competitive running time. TOBAE works by tracking the cumulative density distribution of the data points on a grid and only requires the original data set as input. The clustering problem is solved by automatically finding the optimal density threshold for the clusters. It is applicable to any N-dimensional data set which makes it highly relevant for real world scenarios. The algorithm outperforms state of the art clustering algorithms by the additional feature of automatic noise filtration around clusters. The concept behind the algorithm is explained using the analogy of puddles (’tobae’), which the algorithm is inspired from. This paper provides a detailed algorithm for TOBAE along with the complexity analysis for both time and space. We show experimental results against known data sets and show how TOBAE competes with the best algorithms in the field while providing its own set of advantages.

Journal

Journal of ClassificationSpringer Journals

Published: Mar 3, 2015

There are no references for this article.