forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online
The workshop aims to discuss key issues and practices of semantic mining. Thanks to the initiatives of the Linked Open Data and robust techniques for semantic annotation of Web, social, and sensor data, more semantic data is available. Many research efforts have been directed toward demonstrating semantic techniques to analyze and mine this growing resource. The workshop will provide a cross-disciplinary forum for researchers to showcase their innovation and efforts, and to further enhance existing bounds and create new connections among different communities. Here we solicit contributions on researches and practices of mining data semantics including theory, algorithms, and applications from computer science, life science, healthcare and other domains.
Mloss is a community effort at producing reproducible research via open source software, open access to data and results, and open standards for interchange.
At the highest level of description, this book is about data mining. However,
it focuses on data mining of very large amounts of data, that is, data so large
it does not fit in main memory. Because of the emphasis on size, many of our
examples are about the Web or data derived from the Web. Further, the book
takes an algorithmic point of view: data mining is about applying algorithms
to data, rather than using data to “train” a machine-learning engine of some
sort.
Kaggle is a platform for data prediction competitions. Companies, organizations and researchers post their data and have it scrutinized by the world's best statisticians.
An AOL researcher inadverntly released a database of 21 million search queries to the Web. It's been snagged and is providing a valuable trove of sociological search behavior ... and potential privacy intrusion if the ID numbers are ever associated with u