Clusterdv: a simple density-based clustering method that is robust, general and automatic

Marques, João C.; Orger, Michael B.; Wren, ed., Jonathan

doi:10.1093/bioinformatics/bty932

Title: Clusterdv: a simple density-based clustering method that is robust, general and automatic

Journal Article · 07 November 2018 · Bioinformatics

DOI: https://doi.org/10.1093/bioinformatics/bty932 · OSTI ID:1562941

Marques, João C.;

; Wren, ed., Jonathan

Abstract Motivation How to partition a dataset into a set of distinct clusters is a ubiquitous and challenging problem. The fact that data vary widely in features such as cluster shape, cluster number, density distribution, background noise, outliers and degree of overlap, makes it difficult to find a single algorithm that can be broadly applied. One recent method, clusterdp, based on search of density peaks, can be applied successfully to cluster many kinds of data, but it is not fully automatic, and fails on some simple data distributions. Results We propose an alternative approach, clusterdv, which estimates density dips between points, and allows robust determination of cluster number and distribution across a wide range of data, without any manual parameter adjustment. We show that this method is able to solve a range of synthetic and experimental datasets, where the underlying structure is known, and identifies consistent and meaningful clusters in new behavioral data. Availability and implementation The clusterdv is implemented in Matlab. Its source code, together with example datasets are available on: https://github.com/jcbmarques/clusterdv. Supplementary information Supplementary data are available at Bioinformatics online.

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Sponsoring Organization:: USDOE Office of Nuclear Energy (NE), Fuel Cycle Technologies (NE-5); USDOE Office of Nuclear Energy (NE), Nuclear Fuel Cycle and Supply Chain

OSTI ID:: 1562941

Journal Information:: Bioinformatics, Journal Name: Bioinformatics Journal Issue: 12 Vol. 35; ISSN 1367-4803

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United Kingdom

Language:: English

References (26)

Structure of the Zebrafish Locomotor Repertoire Revealed with Unsupervised Behavioral Clustering Marques, João C.; Lackner, Simone; Félix, Rita Current Biology, Vol. 28, Issue 2 https://doi.org/10.1016/j.cub.2017.12.002	journal	January 2018
Iterative shrinking method for clustering problems Fränti, Pasi; Virmajoki, Olli Pattern Recognition, Vol. 39, Issue 5 https://doi.org/10.1016/j.patcog.2005.09.012	journal	May 2006
Robust path-based spectral clustering Chang, Hong; Yeung, Dit-Yan Pattern Recognition, Vol. 41, Issue 1 https://doi.org/10.1016/j.patcog.2007.04.010	journal	January 2008
Data clustering: 50 years beyond K-means Jain, Anil K. Pattern Recognition Letters, Vol. 31, Issue 8 https://doi.org/10.1016/j.patrec.2009.09.011	journal	June 2010
:{Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data Monti, Stefano Machine Learning, Vol. 52, Issue 1/2, p. 91-118 https://doi.org/10.1023/A:1023949509487	journal	January 2003
Comparing the performance of biomedical clustering methods Wiwie, Christian; Baumbach, Jan; Röttger, Richard Nature Methods, Vol. 12, Issue 11 https://doi.org/10.1038/nmeth.3583	journal	September 2015
Clustering by fast search and merge of local density peaks for gene expression microarray data Mehmood, Rashid; El-Ashram, Saeed; Bie, Rongfang Scientific Reports, Vol. 7, Issue 1 https://doi.org/10.1038/srep45602	journal	April 2017
Variable Kernel Estimates of Multivariate Densities Breiman, Leo; Meisel, William; Purcell, Edward Technometrics, Vol. 19, Issue 2 https://doi.org/10.1080/00401706.1977.10489521	journal	May 1977
An Information Flow Model for Conflict and Fission in Small Groups Zachary, Wayne W. Journal of Anthropological Research, Vol. 33, Issue 4 https://doi.org/10.1086/jar.33.4.3629752	journal	December 1977
On the shortest spanning subtree of a graph and the traveling salesman problem Kruskal, Joseph B. Proceedings of the American Mathematical Society, Vol. 7, Issue 1 https://doi.org/10.1090/S0002-9939-1956-0078686-7	journal	January 1956
SLINK: An optimally efficient algorithm for the single-link cluster method Sibson, R. The Computer Journal, Vol. 16, Issue 1 https://doi.org/10.1093/comjnl/16.1.30	journal	January 1973
Chameleon: hierarchical clustering using dynamic modeling Karypis, G.; Kumar, V. Computer, Vol. 32, Issue 8 https://doi.org/10.1109/2.781637	journal	January 1999
Gradient-based learning applied to document recognition Lecun, Y.; Bottou, L.; Bengio, Y. Proceedings of the IEEE, Vol. 86, Issue 11 https://doi.org/10.1109/5.726791	journal	January 1998
Complex Wavelet Structural Similarity: A New Image Similarity Index Sampat, M. P.; Gupta, S. IEEE Transactions on Image Processing, Vol. 18, Issue 11 https://doi.org/10.1109/TIP.2009.2025923	journal	November 2009
Least squares quantization in PCM Lloyd, S. IEEE Transactions on Information Theory, Vol. 28, Issue 2 https://doi.org/10.1109/TIT.1982.1056489	journal	March 1982
Survey of Clustering Algorithms Xu, R.; WunschII, D. IEEE Transactions on Neural Networks, Vol. 16, Issue 3 https://doi.org/10.1109/TNN.2005.845141	journal	May 2005
A maximum variance cluster algorithm Veenman, C. J.; Reinders, M. J. T.; Backer, E. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, Issue 9 https://doi.org/10.1109/TPAMI.2002.1033218	journal	September 2002
Estimating the number of clusters in a data set via the gap statistic Tibshirani, Robert; Walther, Guenther; Hastie, Trevor Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 2, p. 411-423 https://doi.org/10.1111/1467-9868.00293	journal	May 2001
Clustering by Passing Messages Between Data Points Frey, B. J.; Dueck, D. Science, Vol. 315, Issue 5814 https://doi.org/10.1126/science.1136800	journal	February 2007
Clustering by fast search and find of density peaks Rodriguez, A.; Laio, A. Science, Vol. 344, Issue 6191 https://doi.org/10.1126/science.1242072	journal	June 2014
Clustering aggregation Gionis, Aristides; Mannila, Heikki; Tsaparas, Panayiotis ACM Transactions on Knowledge Discovery from Data, Vol. 1, Issue 1 https://doi.org/10.1145/1217299.1217303	journal	March 2007
OPTICS: ordering points to identify the clustering structure Ankerst, Mihael; Breunig, Markus M.; Kriegel, Hans-Peter ACM SIGMOD Record, Vol. 28, Issue 2 https://doi.org/10.1145/304181.304187	journal	June 1999
Lower Bounds for the Partitioning of Graphs Donath, W. E.; Hoffman, A. J. IBM Journal of Research and Development, Vol. 17, Issue 5 https://doi.org/10.1147/rd.175.0420	journal	September 1973
Fast clustering using adaptive density peak detection Wang, Xiao-Feng; Xu, Yifan Statistical Methods in Medical Research, Vol. 26, Issue 6 https://doi.org/10.1177/0962280215609948	journal	October 2015
FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data Fu, Limin; Medico, Enzo BMC Bioinformatics, Vol. 8, Issue 1 https://doi.org/10.1186/1471-2105-8-3	journal	January 2007
Sensorimotor Gating in Larval Zebrafish Burgess, H. A.; Granato, M. Journal of Neuroscience, Vol. 27, Issue 18 https://doi.org/10.1523/JNEUROSCI.0615-07.2007	journal	May 2007

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