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The Web-its resources and users-offers a wealth of information for data mining and knowledge discovery. Up to now, a great deal of work has been done applying data mining and machine learning methods to discover novel and useful knowledge on the Web. However, many techniques aim only at extracting knowledge for human users to view and use. Recently, more and more work addresses Web for knowledge that computer systems will use. You can apply such actionable knowledge back to the Web for measurable performance improvements. This special issue of <em>IEEE Intelligent Systems</em> features five articles that address the problem of actionable Web mining.
actionable knowledge, data mining, World Wide Web, collaborative filtering, content-based image retrieval, information extraction, crawler

Q. Yang, X. Wu and C. A. Knoblock, "Guest Editors' Introduction: Mining Actionable Knowledge on the Web," in IEEE Intelligent Systems, vol. 19, no. , pp. 30-31, 2004.
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