Details
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9781509042401 |
| Publication status | Published - 9 Feb 2017 |
| Externally published | Yes |
| Event | 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece Duration: 6 Dec 2016 → 9 Dec 2016 |
Publication series
| Name | 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 |
|---|
Abstract
The class of density-based clustering algorithms excels in detecting clusters of arbitrary shape. DBSCAN, the most common representative, has been demonstrated to be useful in a lot of applications. Still the algorithm suffers from two drawbacks, namely a non-trivial parameter estimation for a given dataset and the limitation to data sets with constant cluster density. The first was already addressed in our previous work, where we presented two hierarchical implementations of DBSCAN. In combination with a simple optimization procedure, those proofed to be useful in detecting appropriate parameter estimates based on an objective function. However, our algorithm was not capable of producing clusters of differing density. In this work we will use the hierarchical information to extract variable density clusters and nested cluster structures. Our evaluation shows that the clustering approach based on edge-lengths of the dendrogram or based on area estimates successfully detects clusters of arbitrary shape and density.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Decision Sciences(all)
- Information Systems and Management
- Mathematics(all)
- Control and Optimization
- Computer Science(all)
- Artificial Intelligence
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2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7849925 (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Variable density based clustering
AU - Dockhorn, Alexander
AU - Braune, Christian
AU - Kruse, Rudolf
PY - 2017/2/9
Y1 - 2017/2/9
N2 - The class of density-based clustering algorithms excels in detecting clusters of arbitrary shape. DBSCAN, the most common representative, has been demonstrated to be useful in a lot of applications. Still the algorithm suffers from two drawbacks, namely a non-trivial parameter estimation for a given dataset and the limitation to data sets with constant cluster density. The first was already addressed in our previous work, where we presented two hierarchical implementations of DBSCAN. In combination with a simple optimization procedure, those proofed to be useful in detecting appropriate parameter estimates based on an objective function. However, our algorithm was not capable of producing clusters of differing density. In this work we will use the hierarchical information to extract variable density clusters and nested cluster structures. Our evaluation shows that the clustering approach based on edge-lengths of the dendrogram or based on area estimates successfully detects clusters of arbitrary shape and density.
AB - The class of density-based clustering algorithms excels in detecting clusters of arbitrary shape. DBSCAN, the most common representative, has been demonstrated to be useful in a lot of applications. Still the algorithm suffers from two drawbacks, namely a non-trivial parameter estimation for a given dataset and the limitation to data sets with constant cluster density. The first was already addressed in our previous work, where we presented two hierarchical implementations of DBSCAN. In combination with a simple optimization procedure, those proofed to be useful in detecting appropriate parameter estimates based on an objective function. However, our algorithm was not capable of producing clusters of differing density. In this work we will use the hierarchical information to extract variable density clusters and nested cluster structures. Our evaluation shows that the clustering approach based on edge-lengths of the dendrogram or based on area estimates successfully detects clusters of arbitrary shape and density.
UR - http://www.scopus.com/inward/record.url?scp=85015987657&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2016.7849925
DO - 10.1109/SSCI.2016.7849925
M3 - Conference contribution
AN - SCOPUS:85015987657
T3 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Y2 - 6 December 2016 through 9 December 2016
ER -