RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates
A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse nearest neighbor counts as an estimate of observation density. Clustering is performed using a DBSCAN-like approach based on k nearest neighbor graph traversals through dense observations. RNN-DBSCAN is preferable...
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| Vydáno v: | IEEE transactions on knowledge and data engineering Ročník 30; číslo 6; s. 1109 - 1121 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
01.06.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 1041-4347, 1558-2191 |
| On-line přístup: | Získat plný text |
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| Abstract | A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse nearest neighbor counts as an estimate of observation density. Clustering is performed using a DBSCAN-like approach based on k nearest neighbor graph traversals through dense observations. RNN-DBSCAN is preferable to the popular density-based clustering algorithm DBSCAN in two aspects. First, problem complexity is reduced to the use of a single parameter (choice of k nearest neighbors), and second, an improved ability for handling large variations in cluster density (heterogeneous density). The superiority of RNN-DBSCAN is demonstrated on several artificial and real-world datasets with respect to prior work on reverse nearest neighbor based clustering approaches (RECORD, IS-DBSCAN, and ISB-DBSCAN) along with DBSCAN and OPTICS. Each of these clustering approaches is described by a common graph-based interpretation wherein clusters of dense observations are defined as connected components, along with a discussion on their computational complexity. Heuristics for RNN-DBSCAN parameter selection are presented, and the effects of k on RNN-DBSCAN clusterings discussed. Additionally, with respect to scalability, an approximate version of RNN-DBSCAN is presented leveraging an existing approximate k nearest neighbor technique. |
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| AbstractList | A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse nearest neighbor counts as an estimate of observation density. Clustering is performed using a DBSCAN-like approach based on k nearest neighbor graph traversals through dense observations. RNN-DBSCAN is preferable to the popular density-based clustering algorithm DBSCAN in two aspects. First, problem complexity is reduced to the use of a single parameter (choice of k nearest neighbors), and second, an improved ability for handling large variations in cluster density (heterogeneous density). The superiority of RNN-DBSCAN is demonstrated on several artificial and real-world datasets with respect to prior work on reverse nearest neighbor based clustering approaches (RECORD, IS-DBSCAN, and ISB-DBSCAN) along with DBSCAN and OPTICS. Each of these clustering approaches is described by a common graph-based interpretation wherein clusters of dense observations are defined as connected components, along with a discussion on their computational complexity. Heuristics for RNN-DBSCAN parameter selection are presented, and the effects of k on RNN-DBSCAN clusterings discussed. Additionally, with respect to scalability, an approximate version of RNN-DBSCAN is presented leveraging an existing approximate k nearest neighbor technique. |
| Author | Cios, Krzysztof Bryant, Avory |
| Author_xml | – sequence: 1 givenname: Avory orcidid: 0000-0001-9653-6704 surname: Bryant fullname: Bryant, Avory email: bryantac@vcu.edu organization: Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284 – sequence: 2 givenname: Krzysztof surname: Cios fullname: Cios, Krzysztof email: kcios@vcu.edu organization: Virginia Commonwealth University, Richmond, VA23284 |
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| Title | RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates |
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