TY - BOOK AU - A2 - Aggarwal, Charu C. A2 - Reddy, Chandan K. T1 - Data Clustering: Algorithms and Applications PB - CRC Press C1 - PY - 2014/ VL - IS - SP - EP - UR - http://www.charuaggarwal.net/clusterbook.pdf DO - KW - clustering KW - toread L1 - SN - 978-1-46-655821-2 N1 - dblp: books/crc/aggarwal2013 N1 - AB - ER - TY - BOOK AU - Tango, Toshiro A2 - T1 - Statistical Methods for Disease Clustering PB - Springer New York C1 - New York, NY PY - 2010/ VL - IS - SP - EP - UR - http://scans.hebis.de/HEBCGI/show.pl?22114256_aub.html DO - KW - bachelor KW - clustering KW - disease KW - kursarbeit KW - literaturliste L1 - SN - 1441915729 (Sekundärausgabe) N1 - N1 - AB - 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 ER - TY - CONF AU - Stumme, G. AU - Taouil, R. AU - Bastide, Y. AU - Lakhal, L. A2 - Klinkenberg, R. A2 - Rüping, S. A2 - Fick, A. A2 - Henze, N. A2 - Herzog, C. A2 - Molitor, R. A2 - Schröder, O. T1 - Conceptual Clustering with Iceberg Concept Lattices T2 - Proc. GI-Fachgruppentreffen Maschinelles Lernen (FGML'01) PB - C1 - Universität Dortmund 763 PY - 2001/october CY - VL - IS - SP - EP - UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2001/FGML01.pdf DO - KW - 2001 KW - analysis KW - closed KW - clustering KW - concept KW - conceptual KW - discovery KW - fca KW - formal KW - iceberg KW - itemsets KW - kdd KW - knowledge KW - lattices L1 - SN - N1 - Publications of Gerd Stumme N1 - AB - ER - TY - JOUR AU - Jain, A. K. AU - Murty, M. N. AU - Flynn, P. J. T1 - Data Clustering: A Review JO - ACM Comput. Surv. PY - 1999/10 VL - 31 IS - 3 SP - 264 EP - 323 UR - http://doi.acm.org/10.1145/331499.331504 DO - 10.1145/331499.331504 KW - clustering KW - overview KW - review L1 - SN - N1 - Data clustering N1 - AB - 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. ER - TY - CONF AU - Zhang, Tian AU - Ramakrishnan, Raghu AU - Livny, Miron A2 - T1 - BIRCH: An Efficient Data Clustering Method for Very Large Databases T2 - Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data PB - ACM C1 - New York, NY, USA PY - 1996/ CY - VL - IS - SP - 103 EP - 114 UR - http://doi.acm.org/10.1145/233269.233324 DO - 10.1145/233269.233324 KW - birch KW - clustering KW - kdd L1 - SN - 0-89791-794-4 N1 - BIRCH N1 - AB - ER -