@book{tango2010statistical,
  abstract = {The development of powerful computing environment and the geographical information system (GIS) in recent decades has thrust the analysis of geo-referenced disease incidence data into the mainstream of spatial epidemiology. This book offers a modern perspective on statistical methods for detecting disease clustering, an indispensable procedure to find a statistical evidence on aetiology of the disease under study.

With increasing public health concerns about environmental risks, the need for sophisticated methods for analyzing spatial health events is immediate. Furthermore, the research area of statistical methods for disease clustering now attracts a wide audience due to the perceived need to implement wide-ranging monitoring systems to detect possible health-related events such as the occurrence of the severe acute respiratory syndrome (SARS), pandemic influenza and bioterrorism},
  address = {New York, NY},
  author = {Tango, Toshiro},
  edition = 1,
  format = {ebook},
  interhash = {20ae4f5773d215aded8304c02a071251},
  intrahash = {aa3f9cbf0e4ff83c7323cb2f1d7422eb},
  isbn = {1441915729 (Sekundärausgabe)},
  primaryauthor = {Tango, Toshiro},
  publisher = {Springer New York},
  series = {Statistics for Biology and Health},
  shorttitle = {Statistical Methods for Disease Clustering},
  subtitle = {[Elektronische Ressource] / by Toshiro Tango},
  title = {Statistical Methods for Disease Clustering},
  titlestatement = {by Toshiro Tango},
  uniqueid = {HEB221142568},
  url = {http://scans.hebis.de/HEBCGI/show.pl?22114256_aub.html},
  year = 2010
}

@book{DBLP:books/crc/aggarwal2013,
  bibsource = {DBLP, http://dblp.uni-trier.de},
  editor = {Aggarwal, Charu C. and Reddy, Chandan K.},
  ee = {http://www.crcpress.com/product/isbn/9781466558212, http://www.charuaggarwal.net/clusterbook.pdf},
  interhash = {5f150f838457faaa3805b0ed034c845f},
  intrahash = {7f1541e5800e6c36c67dd6bc0ef64ba7},
  isbn = {978-1-46-655821-2},
  publisher = {CRC Press},
  title = {Data Clustering: Algorithms and Applications},
  url = {http://www.charuaggarwal.net/clusterbook.pdf},
  year = 2014
}

@article{Jain:1999:DCR:331499.331504,
  abstract = {Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.},
  acmid = {331504},
  address = {New York, NY, USA},
  author = {Jain, A. K. and Murty, M. N. and Flynn, P. J.},
  doi = {10.1145/331499.331504},
  hans = {otto},
  interhash = {5113b61d428d4de4423182e5f2b2f468},
  intrahash = {bd7234f7139a1651acfaed57b5c2551f},
  issn = {0360-0300},
  issue_date = {Sept. 1999},
  journal = {ACM Comput. Surv.},
  month = sep,
  number = 3,
  numpages = {60},
  pages = {264--323},
  publisher = {ACM},
  title = {Data Clustering: A Review},
  url = {http://doi.acm.org/10.1145/331499.331504},
  volume = 31,
  year = 1999
}

@inproceedings{stumme01conceptualclustering,
  address = {Universität Dortmund 763},
  author = {Stumme, G. and Taouil, R. and Bastide, Y. and Lakhal, L.},
  booktitle = {Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)},
  editor = {Klinkenberg, R. and Rüping, S. and Fick, A. and Henze, N. and Herzog, C. and Molitor, R. and Schröder, O.},
  interhash = {c99f2ae002435208c58f9244d298a10b},
  intrahash = {f4ec21d5f63dbc213a3a6eae076c4b62},
  month = {October},
  title = {Conceptual Clustering with Iceberg Concept Lattices},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf},
  year = 2001
}

@inproceedings{zhang1996birch,
  acmid = {233324},
  address = {New York, NY, USA},
  author = {Zhang, Tian and Ramakrishnan, Raghu and Livny, Miron},
  booktitle = {Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data},
  doi = {10.1145/233269.233324},
  interhash = {bd3d8e33e8785ecf66408081db016ca4},
  intrahash = {250cecc10ceecd05a96bed00b6cf0fd7},
  isbn = {0-89791-794-4},
  location = {Montreal, Quebec, Canada},
  numpages = {12},
  pages = {103--114},
  publisher = {ACM},
  series = {SIGMOD '96},
  title = {BIRCH: An Efficient Data Clustering Method for Very Large Databases},
  url = {http://doi.acm.org/10.1145/233269.233324},
  year = 1996
}