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Data Mining

Here is a one-page printable PDF version of the CFP for this track.

The phenomenal growth of the web has led to an ever-increasing volume of data and information being published in web-accessible formats. Research in web data mining aims to develop new techniques to derive actionable knowledge and information from these sources. Due to heterogeneity and lack of structure in web data, automated discovery of targeted or unexpected knowledge is a challenging task. It calls for novel methods that draw from a wide range of fields such as data mining, machine learning, natural language processing, statistics, databases, and information retrieval.

For the data mining track, we invite original and high quality submissions addressing all aspects of web data mining. The relevant topics include, but are not restricted to, the following:

  • Novel classification or clustering methods for web data
  • Mining web content and link structure
  • Web log mining and web traffic analysis
  • Building user profiles and providing recommendations
  • Spatio-temporal analysis of blogs, reviews, and discussions
  • Change detection and monitoring methods for web data
  • Entity and relationship extraction and disambiguation
  • Privacy issues in web mining
  • Data integration and data cleaning
  • Integrating linguistic and domain knowledge in web mining

Paper formatting requirements are provided on the submissions page.

Track Chair: Roberto Bayardo (Google, USA)

Deputy Chair: Kyuseok Shim (Seoul National University, Korea)

Program Committee

  • Charu Aggarwal (IBM T.J. Watson Research Center, USA)
  • Rakesh Agrawal (Microsoft Search Labs, USA)
  • Wray Buntine (Helsinki Institute of Information Technology, Finland)
  • Ming-Syan Chen (National Taiwan University, Taiwan)
  • Wei Fan (IBM T.J. Watson Research Center, USA)
  • Aristides Gionis (University of Helsinki, Finland)
  • Marko Grobelnik (J. Stefan Institute, Slovenia)
  • Jiawei Han (University of Illinois at Urbana-Champaign, USA)
  • Andreas Hotho (University of Kassel, Germany)
  • Masaru Kitsuregawa (University of Tokyo, Japan)
  • Dongwon Lee (Penn State University, USA)
  • Bing Liu (University of Illinois at Chicago, USA)
  • Mark Manasse (Microsoft Research, USA)
  • Dunja Mladenic (J. Stefan Institute, Slovenia)
  • Beng Chin Ooi (National University of Singapore, Singapore)
  • Jung Soo Park (Sungshin University, Korea)
  • Jian Pei (Simon Fraser University, Canada)
  • Sridhar Rajagopalan (IBM Almaden, USA)
  • Raghu Ramakrishnan (University of Wisconsin/Yahoo!, USA)
  • Rajeev Rastogi (Bell Laboratories, USA)
  • Deepak Ravichandran (Google, USA)
  • Barry Smyth (University College Dublin, Ireland)
  • Myra Spiliopoulou (Universitat Magdeburg, Germany)
  • Ramakrishnan Srikant (Google, USA)
  • Andrew Tomkins (Yahoo!, USA)
  • Ji-Rong Wen (Microsoft Research Asia, China)
  • Jeffrey Xu Yu (The Chinese University of Hong Kong, Hong Kong)