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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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 30; no. 6; pp. 1109 - 1121
Main Authors: Bryant, Avory, Cios, Krzysztof
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Approximation algorithms
Clustering
Clustering algorithms
Complexity
Complexity theory
Density
density estimation robust algorithm
Indexes
Measurement
nearest neighbor searches
Parameters
pattern analysis
pattern clustering
Unsupervised learning
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary: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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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2017.2787640