@book{reference/rsh/2011,, title = {Recommender Systems Handbook}, editor = {Ricci, Francesco and Rokach, Lior and Shapira, Bracha and Kantor, Paul B.}, booktitle = {Recommender Systems Handbook}, publisher = {Springer}, year = {2011}, url = {http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html}, isbn = {978-0-387-85819-7}, keywords = {*****, handbook, overview, recommender, survey, systems} }

%0 = book %B = Recommender Systems Handbook %D = 2011 %I = Springer %T = Recommender Systems Handbook %U = http://dblp.uni-trier.de/db/reference/rsh/rsh2011.html

Subgroup discovery is a data mining technique which extracts interesting rules with respect to a target variable. An important characteristic of this task is the combination of predictive and descriptive induction. An overview related to the task of subgroup discovery is presented. This review focuses on the foundations, algorithms, and advanced studies together with the applications of subgroup discovery presented throughout the specialised bibliography.

@article{springerlink:10.1007/s10115-010-0356-2, author = {Herrera, Franciso and Carmona, Cristóbal and González, Pedro and del Jesus, María}, title = {An overview on subgroup discovery: foundations and applications}, journal = {Knowledge and Information Systems}, publisher = {Springer London}, year = {2010}, pages = {1-31}, url = {http://dx.doi.org/10.1007/s10115-010-0356-2}, doi = {10.1007/s10115-010-0356-2}, issn = {0219-1377}, keywords = {application, discovery, introduction, overview, subgroup, survey}, abstract = {Subgroup discovery is a data mining technique which extracts interesting rules with respect to a target variable. An important characteristic of this task is the combination of predictive and descriptive induction. An overview related to the task of subgroup discovery is presented. This review focuses on the foundations, algorithms, and advanced studies together with the applications of subgroup discovery presented throughout the specialised bibliography.} }

%0 = article %A = Herrera, Franciso and Carmona, Cristóbal and González, Pedro and del Jesus, María %D = 2010 %I = Springer London %T = An overview on subgroup discovery: foundations and applications %U = http://dx.doi.org/10.1007/s10115-010-0356-2

@article{hotho-etal-ldv-2005, author = {Hotho, Andreas and Nürnberger, Andreas and Paaß, Gerhard}, title = { A Brief Survey of Text Mining}, journal = {LDV Forum - GLDV Journal for Computational Linguistics and Language Technology}, year = {2005}, volume = {20}, number = {1}, pages = {19-62}, url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2005/hotho05TextMining.pdf}, issn = {0175-1336}, keywords = {2005, SumSchool06, mining, myown, ontology, overview, survey, text, tm} }

%0 = article %A = Hotho, Andreas and Nürnberger, Andreas and Paaß, Gerhard %D = 2005 %T = A Brief Survey of Text Mining %U = http://www.kde.cs.uni-kassel.de/hotho/pub/2005/hotho05TextMining.pdf

@book{staab04handbook,, title = {Handbook on Ontologies}, editor = {Staab, S. and Studer, R.}, publisher = {Springer}, address = {Berlin, DE}, year = {2003}, keywords = {web, ontology, SumSchool06, overview, semantic} }

%0 = book %C = Berlin, DE %D = 2003 %I = Springer %T = Handbook on Ontologies

@book{Fensel:2002,, title = {Spinning the Semantic Web: Bringing the World Wide Web
to Its Full Potential}, editor = {Fensel, Dieter and Wahlster, Wolfgang and Lieberman, Henry and Hendler, James}, publisher = {MIT Press}, year = {2002}, keywords = {web, SumSchool06, overview, semantic} }

%0 = book %D = 2002 %I = MIT Press %T = Spinning the Semantic Web: Bringing the World Wide Web
to Its Full Potential

@book{hoeppner1999fuzzy, author = {Höppner, Frank and Klawonn, Frank and Kruse, Rudolf and Runkler, Thomas}, title = {Fuzzy Cluster Analysis}, publisher = {John Wiley & Sons, Inc.}, year = {1999}, isbn = {3-540-40317-5}, keywords = {clustering, evaluation, fuzzy, overview} }

%0 = book %A = Höppner, Frank and Klawonn, Frank and Kruse, Rudolf and Runkler, Thomas %D = 1999 %I = John Wiley & Sons, Inc. %T = Fuzzy Cluster Analysis

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