[ACM Press the 2004 ACM SIGKDD international conference - Seattle, WA, USA (2004.08.22-2004.08.25)] Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04 - A probabilistic framework for semi-supervised clustering

[ACM Press the 2004 ACM SIGKDD internati

✍ Basu, Sugato; Bilenko, Mikhail; Mooney, Raymond J. 📂 Article 📅 2004 🏛 ACM Press ⚖ 183 KB

Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. In recent years, a number of algorithms have been proposed for enhancing clustering quality by employing such supe

[ACM Press the 2004 ACM SIGKDD internati

[ACM Press the 2004 ACM SIGKDD international conference - Seattle, WA, USA (2004.08.22-2004.08.25)] Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04 - k-TTP

✍ Gilburd, Bobi; Schuster, Assaf; Wolff, Ran 📂 Article 📅 2004 🏛 ACM Press ⚖ 178 KB

[ACM Press the 2004 ACM SIGKDD internati

[ACM Press the 2004 ACM SIGKDD international conference - Seattle, WA, USA (2004.08.22-2004.08.25)] Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04 - Kernel k-means

✍ Dhillon, Inderjit S.; Guan, Yuqiang; Kulis, Brian 📂 Article 📅 2004 🏛 ACM Press ⚖ 251 KB

[ACM Press the 2004 ACM SIGKDD internati

[ACM Press the 2004 ACM SIGKDD international conference - Seattle, WA, USA (2004.08.22-2004.08.25)] Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04 - Towards parameter-free data mining

✍ Keogh, Eamonn; Lonardi, Stefano; Ratanamahatana, Chotirat Ann 📂 Article 📅 2004 🏛 ACM Press ⚖ 753 KB

[ACM Press the 2004 ACM SIGKDD internati

[ACM Press the 2004 ACM SIGKDD international conference - Seattle, WA, USA (2004.08.22-2004.08.25)] Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04 - Probabilistic author-topic models for information discovery

✍ Steyvers, Mark; Smyth, Padhraic; Rosen-Zvi, Michal; Griffiths, Thomas 📂 Article 📅 2004 🏛 ACM Press ⚖ 316 KB

[ACM Press the 2004 ACM SIGKDD internati

[ACM Press the 2004 ACM SIGKDD international conference - Seattle, WA, USA (2004.08.22-2004.08.25)] Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04 - Probabilistic author-topic models for information discovery

✍ Steyvers, Mark; Smyth, Padhraic; Rosen-Zvi, Michal; Griffiths, Thomas 📂 Article 📅 2004 🏛 ACM Press ⚖ 316 KB

We propose a new unsupervised learning technique for extracting information from large text collections. We model documents as if they were generated by a two-stage stochastic process. Each author is represented by a probability distribution over topics, and each topic is represented as a probabilit