The Knowledge Discovery Machine Learning (KDML) group focuses on the neighboring subfields of computer science known as knowledge discovery in databases (KDD, sometimes referred to simply as data mining) and machine learning (ML). For us, these fields include on the one hand the automated analysis of large data sets using intelligent algorithms that are capable of extracting from the collected data hidden knowledge in order to produce models that can be used for prediction and decision making. On the other hand, they also include algorithms and systems that are capable of learning from experience and adapting to their environment or their users.
Platform for sharing and evaluation of intelligent algorithms. Data mining data, experiments, datasets, performance analysis, data repository, challenges. Research and applications, prediction. Data mining and machine learning
Mahout currently has Collaborative Filtering User and Item based recommenders K-Means, Fuzzy K-Means clustering Mean Shift clustering Dirichlet process clustering Latent Dirichlet Allocation Singular value decomposition Parallel Frequent Pattern mining Complementary Naive Bayes classifier Random forest decision tree based classifier High performance java collections (previously colt collections) A vibrant community and many more cool stuff to come by this summer thanks to Google summer of code
Mloss is a community effort at producing reproducible research via open source software, open access to data and results, and open standards for interchange.
A. Coates, H. Lee, и A. Ng. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, том 15 из JMLR Workshop and Conference Proceedings, стр. 215--223. JMLR W&CP, (2011)
