@Article{hahsler2019dbscan,
    title = {{dbscan}: Fast Density-Based Clustering with {R}},
    author = {Michael Hahsler and Matthew Piekenbrock and Derek Doran},
    journal = {Journal of Statistical Software},
    year = {2019},
    volume = {91},
    number = {1},
    pages = {1--30},
    doi = {10.18637/jss.v091.i01},
  }


@inproceedings{ester1996density,
  title={A density-based algorithm for discovering clusters in large spatial databases with noise.},
  author={Ester, Martin and Kriegel, Hans-Peter and Sander, J{\"o}rg and Xu, Xiaowei and others},
  booktitle={Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96)},
  pages={226--231},
  year={1996},
  url = {https://dl.acm.org/doi/10.5555/3001460.3001507}
}

@Manual{dbscan-R,
  title = {dbscan: Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms},
  author = {Michael Hahsler and Matthew Piekenbrock},
  note = {R package version 0.9-8.2},
  year={2016}
}
%% Original OPTICS paper
%% -----------------------------------------------------------------------------
@inproceedings{ankerst1999optics,
  title={OPTICS: ordering points to identify the clustering structure},
  author={Ankerst, Mihael and Breunig, Markus M and Kriegel, Hans-Peter and Sander, J{\"o}rg},
  booktitle={ACM Sigmod Record},
  volume={28},
  number={2},
  pages={49--60},
  year={1999},
  organization={ACM},
  doi = {10.1145/304181.304187}
}

% OPTICS cluster extraction improvements
% -----------------------------------------------------------------------------
@inproceedings{DBLP:conf/lwa/SchubertG18,
  author    = {Erich Schubert and
               Michael Gertz},
  title     = {Improving the Cluster Structure Extracted from {OPTICS} Plots},
  booktitle = {Lernen, Wissen, Daten, Analysen (LWDA 2018)},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2191},
  pages     = {318--329},
  publisher = {CEUR-WS.org},
  year      = {2018}
}

% Original LOF paper
% -----------------------------------------------------------------------------
@inproceedings{breunig2000lof,
 title={LOF: identifying density-based local outliers},
 author={Breunig, Markus M and Kriegel, Hans-Peter and Ng, Raymond T and Sander, J{\"o}rg},
 booktitle={ACM Int. Conf. on Management of Data},
 volume={29},
 number={2},
 pages={93--104},
 year={2000},
 organization={ACM},
 doi = {10.1145/335191.335388}
}

% 2003 Reachability <--> Dendrograms Conversions Paper
% -----------------------------------------------------------------------------
@inproceedings{sander2003automatic,
 title={Automatic extraction of clusters from hierarchical clustering representations},
 author={Sander, J{\"o}rg and Qin, Xuejie and Lu, Zhiyong and Niu, Nan and Kovarsky, Alex},
 booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
 pages={75--87},
 year={2003},
 organization={Springer}
}

% Original BIRCH paper
% -----------------------------------------------------------------------------
@inproceedings{zhang96,
 title={BIRCH: an efficient data clustering method for very large databases},
 author={Zhang, Tian and Ramakrishnan, Raghu and Livny, Miron},
 booktitle={ACM Sigmod Record},
 volume={25},
 number={2},
 pages={103--114},
 year={1996},
 organization={ACM}
}

% GDBSCAN Paper (Generalized DBSCAN, by Sanders)
% -----------------------------------------------------------------------------
@article{sander1998density,
 title={Density-based clustering in spatial databases: The algorithm gdbscan and its applications},
 author={Sander, J{\"o}rg and Ester, Martin and Kriegel, Hans-Peter and Xu, Xiaowei},
 journal={Data mining and knowledge discovery},
 volume={2},
 number={2},
 pages={169--194},
 year={1998},
 publisher={Springer}
}

% HDBSCAN* Newest Paper
% -----------------------------------------------------------------------------
@article{campello2015hierarchical,
 title={Hierarchical density estimates for data clustering, visualization, and outlier detection},
 author={Campello, Ricardo JGB and Moulavi, Davoud and Zimek, Arthur and Sander, Joerg},
 journal={ACM Transactions on Knowledge Discovery from Data (TKDD)},
 volume={10},
 number={1},
 pages={5},
 year={2015},
 publisher={ACM},
 doi = {10.1145/2733381}
}

% First HDBSCAN* introduction paper, later revised in 2015. The newer one is better.
% -----------------------------------------------------------------------------
@inproceedings{campello2013density,
 title={Density-based clustering based on hierarchical density estimates},
 author={Campello, Ricardo JGB and Moulavi, Davoud and Sander, J{\"o}rg},
 booktitle={Pacific-Asia Conference on Knowledge Discovery and Data Mining},
 pages={160--172},
 year={2013},
 organization={Springer},
 doi = {10.1007/978-3-642-37456-2_14}
}

% The new-ish 'Standard Methodology' paper of that 'tackles the methodological  drawbacks' % of internal clustering validation
% -----------------------------------------------------------------------------
@article{gurrutxaga2011towards,
 title={Towards a standard methodology to evaluate internal cluster validity indices},
 author={Gurrutxaga, Ibai and Muguerza, Javier and Arbelaitz, Olatz and P{\'e}rez, Jes{\'u}s M and Mart{\'\i}n, Jos{\'e} I},
 journal={Pattern Recognition Letters},
 volume={32},
 number={3},
 pages={505--515},
 year={2011},
 publisher={Elsevier}
}

% Original ABACUS - Workaround implementation of mixture modeling for finding
% arbitrary shapes
% -----------------------------------------------------------------------------
@article{gegick2011abacus,
 title={ABACUS: mining arbitrary shaped clusters from large datasets based on backbone identification},
 author={Gegick, M},
 year={2011},
 publisher={SIAM}
}


% Original Silhouette Index Paper
% -----------------------------------------------------------------------------
@article{rousseeuw1987silhouettes,
 title={Silhouettes: a graphical aid to the interpretation and validation of cluster analysis},
 author={Rousseeuw, Peter J},
 journal={Journal of computational and applied mathematics},
 volume={20},
 pages={53--65},
 year={1987},
 publisher={Elsevier}
}

% Extensive Comparative Study of IVMS
% -----------------------------------------------------------------------------
@article{arbelaitz2013extensive,
 title={An extensive comparative study of cluster validity indices},
 author={Arbelaitz, Olatz and Gurrutxaga, Ibai and Muguerza, Javier and P{\'e}rez, Jes{\'u}S M and Perona, I{\~n}Igo},
 journal={Pattern Recognition},
 volume={46},
 number={1},
 pages={243--256},
 year={2013},
 publisher={Elsevier}
}

% Graph Theory measures for Internal Cluster Validation
% -----------------------------------------------------------------------------
@article{pal1997cluster,
 title={Cluster validation using graph theoretic concepts},
 author={Pal, Nikhil R and Biswas, J},
 journal={Pattern Recognition},
 volume={30},
 number={6},
 pages={847--857},
 year={1997},
 publisher={Elsevier}
}

% Rankings of research papers by citation count; used for showing DBSCAN
% popularity
% -----------------------------------------------------------------------------
@misc{acade96:online,
author = {{Microsoft Academic Search}},
title = {Top publications in data mining},
howpublished = {\url{http://academic.research.microsoft.com/RankList?entitytype=1&topDomainID=2&subDomainID=7&last=0&start=1&end=100}},
month = {},
year = {2016},
note = {(Accessed on 08/29/2016)}
}


@misc{PyCluste54:online,
author = {Novikov, Andrei},
title = {PyClustering: PyClustering library},
howpublished = {\url{http://pythonhosted.org/pyclustering/}},
year = {2016},
note = {v.0.6.6}
}


% Hartigans convex density estimation model
% -----------------------------------------------------------------------------
@article{hartigan1987estimation,
 title={Estimation of a convex density contour in two dimensions},
 author={Hartigan, JA},
 journal={Journal of the American Statistical Association},
 volume={82},
 number={397},
 pages={267--270},
 year={1987},
 publisher={Taylor \& Francis}
}

% Bentleys Original KDTree Paper
% -----------------------------------------------------------------------------
@article{bentley1975multidimensional,
 title={Multidimensional binary search trees used for associative searching},
 author={Bentley, Jon Louis},
 journal={Communications of the ACM},
 volume={18},
 number={9},
 pages={509--517},
 year={1975},
 publisher={ACM}
}

% Original CLARANS paper
% -----------------------------------------------------------------------------
@article{ng2002clarans,
 title={CLARANS: A method for clustering objects for spatial data mining},
 author={Ng, Raymond T. and Han, Jiawei},
 journal={IEEE transactions on knowledge and data engineering},
 volume={14},
 number={5},
 pages={1003--1016},
 year={2002},
 publisher={IEEE}
}

% Original DENCLUE paper
% -----------------------------------------------------------------------------
@inproceedings{hinneburg1998efficient,
 title={An efficient approach to clustering in large multimedia databases with noise},
 author={Hinneburg, Alexander and Keim, Daniel A},
 booktitle={KDD},
 volume={98},
 pages={58--65},
 year={1998}
}

% Original Chameleon Paper
% -----------------------------------------------------------------------------
@article{karypis1999chameleon,
 title={Chameleon: Hierarchical clustering using dynamic modeling},
 author={Karypis, George and Han, Eui-Hong and Kumar, Vipin},
 journal={Computer},
 volume={32},
 number={8},
 pages={68--75},
 year={1999},
 publisher={IEEE}
}

% Original CURE algorithm
% -----------------------------------------------------------------------------
@inproceedings{guha1998cure,
 title={CURE: an efficient clustering algorithm for large databases},
 author={Guha, Sudipto and Rastogi, Rajeev and Shim, Kyuseok},
 booktitle={ACM SIGMOD Record},
 volume={27},
 number={2},
 pages={73--84},
 year={1998},
 organization={ACM}
}

% R statistical computing language citation
% -----------------------------------------------------------------------------
@article{team2013r,
 title={R: A language and environment for statistical computing},
 author={Team, R Core and others},
 year={2013},
 publisher={Vienna, Austria}
}

% WEKA
% -----------------------------------------------------------------------------
@article{hall2009weka,
 title={The WEKA data mining software: an update},
 author={Hall, Mark and Frank, Eibe and Holmes, Geoffrey and Pfahringer, Bernhard and Reutemann, Peter and Witten, Ian H},
 journal={ACM SIGKDD explorations newsletter},
 volume={11},
 number={1},
 pages={10--18},
 year={2009},
 publisher={ACM}
}

% SPMF Java Machine Learning Library
% -----------------------------------------------------------------------------
@article{fournier2014spmf,
 title={SPMF: a Java open-source pattern mining library.},
 author={Fournier-Viger, Philippe and Gomariz, Antonio and Gueniche, Ted and Soltani, Azadeh and Wu, Cheng-Wei and Tseng, Vincent S and others},
 journal={Journal of Machine Learning Research},
 volume={15},
 number={1},
 pages={3389--3393},
 year={2014}
}

% Python Scikit Learn
% -----------------------------------------------------------------------------
@article{pedregosa2011scikit,
 title={Scikit-learn: Machine learning in Python},
 author={Pedregosa, Fabian and Varoquaux, Ga{\"e}l and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and others},
 journal={Journal of Machine Learning Research},
 volume={12},
 number={Oct},
 pages={2825--2830},
 year={2011}
}

% MATLAB TOMCAT Toolkit
% -----------------------------------------------------------------------------
@article{daszykowski2007tomcat,
 title={TOMCAT: A MATLAB toolbox for multivariate calibration techniques},
 author={Daszykowski, Micha{\l} and Serneels, Sven and Kaczmarek, Krzysztof and Van Espen, Piet and Croux, Christophe and Walczak, Beata},
 journal={Chemometrics and intelligent laboratory systems},
 volume={85},
 number={2},
 pages={269--277},
 year={2007},
 publisher={Elsevier}
}

% OPTICS code for TOMCAT
% -----------------------------------------------------------------------------
@article{daszykowski2002looking,
 title={Looking for natural patterns in analytical data. 2. Tracing local density with OPTICS},
 author={Daszykowski, Michael and Walczak, Beata and Massart, Desire L},
 journal={Journal of chemical information and computer sciences},
 volume={42},
 number={3},
 pages={500--507},
 year={2002},
 publisher={ACS Publications}
}

% Java ML library
% -----------------------------------------------------------------------------
@comment{ Abeel, T.; de Peer, Y. V. & Saeys, Y. Java-ML: A Machine Learning
          Library, Journal of Machine Learning Research, 2009, 10, 931-934  }
@book{abeel2009journal,
author = "Abeel, T. ; de Peer and Y. V. and Saeys, Y. Java-ML: A Machine Learning Library",
title = "Journal of Machine Learning Research",
publisher = "10",
pages = "931--934",
year = 2009
}


% ELKI
% -----------------------------------------------------------------------------
@article{DBLP:journals/pvldb/SchubertKEZSZ15,
 author    = {Erich Schubert and
              Alexander Koos and
              Tobias Emrich and
              Andreas Z{\"{u}}fle and
              Klaus Arthur Schmid and
              Arthur Zimek},
 title     = {A Framework for Clustering Uncertain Data},
 journal   = {{PVLDB}},
 volume    = {8},
 number    = {12},
 pages     = {1976--1979},
 year      = {2015},
 url       = {http://www.vldb.org/pvldb/vol8/p1976-schubert.pdf},
 timestamp = {Mon, 30 May 2016 12:01:10 +0200},
 biburl    = {http://dblp.uni-trier.de/rec/bib/journals/pvldb/SchubertKEZSZ15},
 bibsource = {dblp computer science bibliography, http://dblp.org}
}

% BIRCH CRAN records
% -----------------------------------------------------------------------------
@misc{CRANPack84:online, author={CRAN}, title = {CRAN - Package birch}, howpublished = {\url{https://cran.r-project.org/web/packages/birch/index.html}}, month = {}, year = {2016}, note = {(Accessed on 09/16/2016)} }

% Spectral Clustering
% ----------------------------------------------------------------------------
@inproceedings{dhillon2004kernel,
 title={Kernel k-means: spectral clustering and normalized cuts},
 author={Dhillon, Inderjit S and Guan, Yuqiang and Kulis, Brian},
 booktitle={Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining},
 pages={551--556},
 year={2004},
 organization={ACM}
}


% Disjoint-set data structure (2 citations)
% -----------------------------------------------------------------------------
@misc{cormen2001introduction,
 title={Introduction to algorithms second edition},
 author={Cormen, Thomas H and Leiserson, Charles E and Rivest, Ronald L and Stein, Clifford},
 year={2001},
 publisher={The MIT Press}
}
@inproceedings{patwary2010experiments,
 title={Experiments on union-find algorithms for the disjoint-set data structure},
 author={Patwary, Md Mostofa Ali and Blair, Jean and Manne, Fredrik},
 booktitle={International Symposium on Experimental Algorithms},
 pages={411--423},
 year={2010},
 organization={Springer}
}

% SUBCLU high-dimensional density based clustering
% -----------------------------------------------------------------------------
@inproceedings{kailing2004density,
 title={Density-connected subspace clustering for high-dimensional data},
 author={Kailing, Karin and Kriegel, Hans-Peter and Kr{\"o}ger, Peer},
 booktitle={Proc. SDM},
 volume={4},
 year={2004},
 organization={SIAM}
}

% DBSCAN KDD Test of Time award
% -----------------------------------------------------------------------------
@misc{SIGKDDNe30:online,
author = {SIGKDD},
title = {SIGKDD News : 2014 SIGKDD Test of Time Award},
howpublished = {\url{http://www.kdd.org/News/view/2014-sigkdd-test-of-time-award}},
month = {},
year = {2014},
note = {(Accessed on 10/10/2016)}
}

% Raftery and Fraley's model-based clustering paper
% -----------------------------------------------------------------------------
@article{fraley2002model,
 title={Model-based clustering, discriminant analysis, and density estimation},
 author={Fraley, Chris and Raftery, Adrian E},
 journal={Journal of the American statistical Association},
 volume={97},
 number={458},
 pages={611--631},
 year={2002},
 publisher={Taylor \& Francis}
}

% FPC: Flexible Procedures for Clustering
% -----------------------------------------------------------------------------
@Manual{fpc,
title = {fpc: Flexible Procedures for Clustering},
author = {Christian Hennig},
year = {2015},
note = {R package version 2.1-10},
url = {https://CRAN.R-project.org/package=fpc},
}

% From the ELKI Benchmarking page
% -----------------------------------------------------------------------------
@article{kriegel2016black,
  title={The (black) art of runtime evaluation: Are we comparing algorithms or implementations?},
  author={Kriegel, Hans-Peter and Schubert, Erich and Zimek, Arthur},
  journal={Knowledge and Information Systems},
  pages={1--38},
  year={2016},
  publisher={Springer}
}

% ANN Library
% -----------------------------------------------------------------------------
@manual{mount1998ann,
 title={ANN: library for approximate nearest neighbour searching},
 author={Mount, David M and Arya, Sunil},
 year={2010},
 url = {http://www.cs.umd.edu/~mount/ANN/},
}

% Rcpp
% -----------------------------------------------------------------------------
@article{eddelbuettel2011rcpp,
 title={Rcpp: Seamless R and C++ integration},
 author={Eddelbuettel, Dirk and Fran{\c{c}}ois, Romain and Allaire, J and Chambers, John and Bates, Douglas and Ushey, Kevin},
 journal={Journal of Statistical Software},
 volume={40},
 number={8},
 pages={1--18},
 year={2011}
}

% ST-DBCAN: SpatioTemporal DBSCAN
% -----------------------------------------------------------------------------
@article{birant2007st,
 title={ST-DBSCAN: An algorithm for clustering spatial--temporal data},
 author={Birant, Derya and Kut, Alp},
 journal={Data \& Knowledge Engineering},
 volume={60},
 number={1},
 pages={208--221},
 year={2007},
 publisher={Elsevier}
}

% DBSCAN History (small relative to actual number of extensions)
% -----------------------------------------------------------------------------
@inproceedings{rehman2014dbscan,
 title={DBSCAN: Past, present and future},
 author={Rehman, Saif Ur and Asghar, Sohail and Fong, Simon and Sarasvady, S},
 booktitle={Applications of Digital Information and Web Technologies (ICADIWT), 2014 Fifth International Conference on the},
 pages={232--238},
 year={2014},
 organization={IEEE}
}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 								 Miscellaneous 						           %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



@article{Gupta2010,
abstract = {A key application of clustering data obtained from sources such as microarrays, protein mass spectroscopy, and phylogenetic profiles is the detection of functionally related genes. Typically, only a small number of functionally related genes cluster into one or more groups, and the rest need to be ignored. For such situations, we present Automated Hierarchical Density Shaving (Auto-HDS), a framework that consists of a fast hierarchical density-based clustering algorithm and an unsupervised model selection strategy. Auto-HDS can automatically select clusters of different densities, present them in a compact hierarchy, and rank individual clusters using an innovative stability criteria. Our framework also provides a simple yet powerful 2D visualization of the hierarchy of clusters that is useful for further interactive exploration. We present results on Gasch and Lee microarray data sets to show the effectiveness of our methods. Additional results on other biological data are included in the supplemental material.},
author = {Gupta, Gunjan and Liu, Alexander and Ghosh, Joydeep},
doi = {10.1109/TCBB.2008.32},
file = {:Users/mpiekenbrock/ResearchLibrary/Automated Hierarchical Density Shaving- A Robust Automated Clustering and Visualization Framework for Large Biological Data Sets.pdf:pdf},
isbn = {1557-9964},
issn = {15455963},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
keywords = {Bioinformatics,Clustering,Data and knowledge visualization,Mining methods and algorithms},
number = {2},
pages = {223--237},
pmid = {20431143},
title = {{Automated hierarchical density shaving: A robust automated clustering and visualization framework for large biological data sets}},
volume = {7},
year = {2010}
}
@article{Ssets,
   author = {P. Fr\"anti and O. Virmajoki},
   title = {Iterative shrinking method for clustering problems},
   journal = {Pattern Recognition},
   year = {2006},
   volume = {39},
   number = {5},
   pages = {761--765}
}

% Path and Spiral based
@article{chang2008robust,
 title={Robust path-based spectral clustering},
 author={Chang, Hong and Yeung, Dit-Yan},
 journal={Pattern Recognition},
 volume={41},
 number={1},
 pages={191--203},
 year={2008},
 publisher={Elsevier}
}

% Compound dataset
@article{zahn1971graph,
 title={Graph-theoretical methods for detecting and describing gestalt clusters},
 author={Zahn, Charles T},
 journal={IEEE Transactions on computers},
 volume={100},
 number={1},
 pages={68--86},
 year={1971},
 publisher={IEEE}
}

% Aggregation dataset
@article{gionis2007clustering,
 title={Clustering aggregation},
 author={Gionis, Aristides and Mannila, Heikki and Tsaparas, Panayiotis},
 journal={ACM Transactions on Knowledge Discovery from Data (TKDD)},
 volume={1},
 number={1},
 pages={4},
 year={2007},
 publisher={ACM}
}

% R15 dataset
@article{veenman2002maximum,
 title={A maximum variance cluster algorithm},
 author={Veenman, Cor J. and Reinders, Marcel J. T. and Backer, Eric},
 journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
 volume={24},
 number={9},
 pages={1273--1280},
 year={2002},
 publisher={IEEE}
}

@inproceedings{reilly2010detection,
 title={Detection and tracking of large number of targets in wide area surveillance},
 author={Reilly, Vladimir and Idrees, Haroon and Shah, Mubarak},
 booktitle={European Conference on Computer Vision},
 pages={186--199},
 year={2010},
 organization={Springer}
}

@inproceedings{jain2005law,
 title={Law, Data clustering: a user’s dilemma},
 author={Jain, Anil K and Martin, HC},
 booktitle={Proceedings of the First international conference on Pattern Recognition and Machine Intelligence},
 year={2005}
}

@article{jain1999review,
   author = {Jain, A. K. and Murty, M. N. and Flynn, P. J.},
   title = {Data Clustering: A Review},
   journal = {ACM Computuing Surveys},
   issue_date = {Sept. 1999},
   volume = {31},
   number = {3},
   month = sep,
   year = {1999},
   issn = {0360-0300},
   pages = {264--323},
   numpages = {60},
   url = {http://doi.acm.org/10.1145/331499.331504},
   doi = {10.1145/331499.331504},
   acmid = {331504},
   publisher = {ACM},
   address = {New York, NY, USA},
}

% Flame data set
@article{fu2007flame,
 title={FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data},
 author={Fu, Limin and Medico, Enzo},
 journal={BMC Bioinformatics},
 volume={8},
 number={1},
 pages={1},
 year={2007},
 publisher={BioMed Central}
}

% Birch dataset
@article{Birchsets,
   author = {T. Zhang and R. Ramakrishnan and M. Livny},
   title = {BIRCH: A new data clustering algorithm and its applications},
   journal = {Data Mining and Knowledge Discovery},
   year = {1997},
   volume = {1},
   number = {2},
   pages = {141--182}
}

@inproceedings{kisilevich2010p,
 title={P-DBSCAN: a density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos},
 author={Kisilevich, Slava and Mansmann, Florian and Keim, Daniel},
 booktitle={Proceedings of the 1st international conference and exhibition on computing for geospatial research \& application},
 pages={38},
 year={2010},
 organization={ACM}
}

@inproceedings{celebi2005mining,
 title={Mining biomedical images with density-based clustering},
 author={Celebi, M Emre and Aslandogan, Y Alp and Bergstresser, Paul R},
 booktitle={International Conference on Information Technology: Coding and Computing (ITCC'05)-Volume II},
 volume={1},
 pages={163--168},
 year={2005},
 organization={IEEE}
}

@inproceedings{ertoz2003finding,
 title={Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data.},
 author={Ert{\"o}z, Levent and Steinbach, Michael and Kumar, Vipin},
 booktitle={SDM},
 pages={47--58},
 year={2003},
 organization={SIAM}
}

@article{Chen2014,
author = {Chen, W and Ji, M H and Wang, J M},
doi = {10.3991/ijoe.v10i6.3881},
file = {:Users/mpiekenbrock/ResearchLibrary/TDBSCAN.pdf:pdf},
issn = {18612121},
journal = {International Journal of Online Engineering},
keywords = {Density-based clustering,Personal travel trajectory,T-DBSCAN,Trip segmentation},
number = {6},
pages = {19--24},
title = {{T-DBSCAN: A spatiotemporal density clustering for GPS trajectory segmentation}},
volume = {10},
year = {2014}
}


@incollection{sander2011density,
 title={Density-based clustering},
 author={Sander, Joerg},
 booktitle={Encyclopedia of Machine Learning},
 pages={270--273},
 year={2011},
 publisher={Springer}
}


% 88 citations
@article{verma2012comparative,
 title={A comparative study of various clustering algorithms in data mining},
 author={Verma, Manish and Srivastava, Mauly and Chack, Neha and Diswar, Atul Kumar and Gupta, Nidhi},
 journal={International Journal of Engineering Research and Applications (IJERA)},
 volume={2},
 number={3},
 pages={1379--1384},
 year={2012}
}

@inproceedings{roy2005approach,
 title={An approach to find embedded clusters using density based techniques},
 author={Roy, Swarup and Bhattacharyya, DK},
 booktitle={International Conference on Distributed Computing and Internet Technology},
 pages={523--535},
 year={2005},
 organization={Springer}
}

@inproceedings{chowdhury2010efficient,
 title={An efficient method for subjectively choosing parameter ‘k’automatically in VDBSCAN (Varied Density Based Spatial Clustering of Applications with Noise) algorithm},
 author={Chowdhury, AK M Rasheduzzaman and Mollah, Md Elias and Rahman, Md Asikur},
 booktitle={Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on},
 volume={1},
 pages={38--41},
 year={2010},
 organization={IEEE}
}

@inproceedings{ghanbarpour2014exdbscan,
 title={EXDBSCAN: An extension of DBSCAN to detect clusters in multi-density datasets},
 author={Ghanbarpour, Asieh and Minaei, Behrooz},
 booktitle={Intelligent Systems (ICIS), 2014 Iranian Conference on},
 pages={1--5},
 year={2014},
 organization={IEEE}
}

@inproceedings{vijayalakshmi2010improved,
 title={Improved varied density based spatial clustering algorithm with noise},
 author={Vijayalakshmi, S and Punithavalli, M},
 booktitle={Computational Intelligence and Computing Research (ICCIC), 2010 IEEE International Conference on},
 pages={1--4},
 year={2010},
 organization={IEEE}
}

@article{Wang2013,
author = {Wang, Wei},
file = {:Users/mpiekenbrock/Downloads/905067f5314e6073d4779c11572bd8c5.pdf:pdf},
isbn = {978-0-9891305-0-9},
keywords = {clustering algorithm,clustering techniques,data mining,derivative,global optimum k,similarity,similarity and minimizes intergroup,there are four basic,vdbscan},
pages = {225--228},
title = {{Improved VDBSCAN With Global Optimum K}},
year = {2013}
}

@article{parvez2012data,
 title={Data set property based ‘K’in VDBSCAN Clustering Algorithm},
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