@book{DBLP:books/crc/aggarwal2013,, title = {Data Clustering: Algorithms and Applications}, editor = {Aggarwal, Charu C. and Reddy, Chandan K.}, publisher = {CRC Press}, year = {2014}, url = {http://www.charuaggarwal.net/clusterbook.pdf}, isbn = {978-1-46-655821-2}, keywords = {clustering, toread} }

%0 = book %D = 2014 %I = CRC Press %T = Data Clustering: Algorithms and Applications %U = http://www.charuaggarwal.net/clusterbook.pdf

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

@book{tango2010statistical, author = {Tango, Toshiro}, title = {Statistical Methods for Disease Clustering}, series = {Statistics for Biology and Health}, publisher = {Springer New York}, address = {New York, NY}, year = {2010}, edition = {1}, url = {http://scans.hebis.de/HEBCGI/show.pl?22114256_aub.html}, isbn = {1441915729 (Sekundärausgabe)}, keywords = {bachelor, clustering, disease, kursarbeit, literaturliste}, 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} }

%0 = book %A = Tango, Toshiro %C = New York, NY %D = 2010 %I = Springer New York %T = Statistical Methods for Disease Clustering %U = http://scans.hebis.de/HEBCGI/show.pl?22114256_aub.html

@inproceedings{stumme01conceptualclustering, author = {Stumme, G. and Taouil, R. and Bastide, Y. and Lakhal, L.}, title = {Conceptual Clustering with Iceberg Concept Lattices}, editor = {Klinkenberg, R. and Rüping, S. and Fick, A. and Henze, N. and Herzog, C. and Molitor, R. and Schröder, O.}, booktitle = {Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01)}, address = {Universität Dortmund 763}, year = {2001}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf}, keywords = {2001, analysis, closed, clustering, concept, conceptual, discovery, fca, formal, iceberg, itemsets, kdd, knowledge, lattices} }

%0 = inproceedings %A = Stumme, G. and Taouil, R. and Bastide, Y. and Lakhal, L. %B = Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01) %C = Universität Dortmund 763 %D = 2001 %T = Conceptual Clustering with Iceberg Concept Lattices %U = http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf

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.

@article{Jain:1999:DCR:331499.331504, author = {Jain, A. K. and Murty, M. N. and Flynn, P. J.}, title = {Data Clustering: A Review}, journal = {ACM Comput. Surv.}, publisher = {ACM}, address = {New York, NY, USA}, year = {1999}, volume = {31}, number = {3}, pages = {264--323}, url = {http://doi.acm.org/10.1145/331499.331504}, doi = {10.1145/331499.331504}, issn = {0360-0300}, keywords = {clustering, overview, review}, 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.} }

%0 = article %A = Jain, A. K. and Murty, M. N. and Flynn, P. J. %C = New York, NY, USA %D = 1999 %I = ACM %T = Data Clustering: A Review %U = http://doi.acm.org/10.1145/331499.331504

@inproceedings{zhang1996birch, author = {Zhang, Tian and Ramakrishnan, Raghu and Livny, Miron}, title = {BIRCH: An Efficient Data Clustering Method for Very Large Databases}, booktitle = {Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data}, series = {SIGMOD '96}, publisher = {ACM}, address = {New York, NY, USA}, year = {1996}, pages = {103--114}, url = {http://doi.acm.org/10.1145/233269.233324}, doi = {10.1145/233269.233324}, isbn = {0-89791-794-4}, keywords = {birch, clustering, kdd} }

%0 = inproceedings %A = Zhang, Tian and Ramakrishnan, Raghu and Livny, Miron %B = Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data %C = New York, NY, USA %D = 1996 %I = ACM %T = BIRCH: An Efficient Data Clustering Method for Very Large Databases %U = http://doi.acm.org/10.1145/233269.233324