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(mobile) microblogging offers great possibilities for advertising (includes a good example) [--> but we will need business intelligence and data mining tec...(mobile) microblogging offers great possibilities for advertising (includes a good example) [--> but we will need business intelligence and data mining technology to take the advance]

Unsupervised Document Classification and Supervised Document Classification

Concept mining is a discipline at the nexus of data mining, text mining, and linguistics, drawing on artificial intelligence and statistics. It aims to ext...Concept mining is a discipline at the nexus of data mining, text mining, and linguistics, drawing on artificial intelligence and statistics. It aims to extract concepts from documents.

This project contains Naive and Fishers bayesian classifiers, as described in Toby Segaran's book "Programming Collective Intelligence." The book has pytho...This project contains Naive and Fishers bayesian classifiers, as described in Toby Segaran's book "Programming Collective Intelligence." The book has python implementations; this is a Java implementation.

JavaNNS is the successor of SNNS. It is based on its computing kernel, with a newly developed, comfortable graphical user interface written in Java set on ...JavaNNS is the successor of SNNS. It is based on its computing kernel, with a newly developed, comfortable graphical user interface written in Java set on top of it. Hence the compatibility with SNNS is achieved, while the platform-independence is increa

Parse::MediaWikiDump is a Perl module created by Triddle that makes accessing the information in a MediaWiki dump file easy.

Wie funktioniert ein Spam-Filter? Diese Frage lässt sich dank des quelloffenen Data-Mining-Frameworks Weka leicht beantworten, indem aufbauend auf vorgefer...Wie funktioniert ein Spam-Filter? Diese Frage lässt sich dank des quelloffenen Data-Mining-Frameworks Weka leicht beantworten, indem aufbauend auf vorgefertigten Komponenten eine maschinelle Textklassifikation in Java implementiert wird. Dieser Beitrag erläutert das notwendige Hintergrundwissen und zeigt den Umgang mit der Weka-Software.

graph mining, data mining

Large set of bibliographic and software resources on Association Rules, including links to datasets and implementations.

Nice overview of papers and approaches concerning data mining in blogs.

for people with large data sets

Sandy Pentland is using data gathered by cell phones to learn more about human behavior and social interactions.

Weka 3: Data Mining Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied di... Weka 3: Data Mining Software in Java Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

Giuseppe Carenini and Raymond T. Ng and Xiaodong Zhou WWW '07: Proceedings of the 16th international conference on World Wide Web, page91--100. New York, NY, USA, ACM, (2007)

Takayuki Fujita and Kentaro Masaki and Fumiaki Suzuki and Kazusuke Maenaka CME 2007: IEEE/ICME International Conference on Complex Medical Engineering, page416-420. (2007)

a (0000)

Behzad Mortazavi-Asl and Jianyong Wang and Helen Pinto and Qiming Chen and Mei-Chun Hsu IEEE Trans. on Knowl. and Data Eng.16(11):1424--1440(2004) Member-Jian Pei and Senior Member-Jiawei Han and Member-Umeshwar Dayal .

Rakesh Agrawal and Tomasz Imieliński and Arun Swami SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, page207--216. New York, NY, USA, ACM Press, (1993)

Daniel J. Solove San Diego Law Review44(289):745(2007) Associate Professor, George Washington University Law School; J.D., Yale Law School. .

Daniel J. Solove San Diego Law Review44(289):745(2007) Associate Professor, George Washington University Law School; J.D., Yale Law School. .

Ricardo Baeza-Yates and Liliana Calderón-Benavides and Cristina González-Caro String Processing and Information Retrieval(2006)

Ingo Mierswa and Michael Wurst and Ralf Klinkenberg and Martin Scholz and Timm Euler KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, page935--940. New York, NY, USA, ACM, (2006)

David Milne and Olena Medelyan and Ian H. Witten ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06), page442--448. (2006)

Ingo Mierswa and Michael Wurst and Ralf Klinkenberg and Martin Scholz and Timm Euler KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, page935--940. New York, NY, USA, ACM, (2006)

Corinna Cortes and Kathleen Fisher and Daryl Pregibon and Anne Rogers KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, page9--17. New York, NY, USA, ACM, (2000)

Dan Bonachea and Kathleen Fisher and Anne Rogers and Frederick Smith DSL'99: Proceedings of the 2nd conference on Conference on Domain-Specific Languages, page13--13. Berkeley, CA, USA, USENIX Association, (1999)

Corinna Cortes and Daryl Pregibon Knowledge Discovery and Data Mining, page174-178. (1998)

Ahmed Metwally and Divyakant Agrawal and Amr El Abbadi WWW '05: Proceedings of the 14th international conference on World Wide Web, page12--21. New York, NY, USA, ACM, (2005)

C.T. Chu and S.K. Kim and Y.A. Lin and Y.Y. Yu and G. Bradski and A.Y. Ng and K. Olukotun (2006)