Lecture 0. Overview of Data Mining and Knowledge Discovery in Databases (KDD). Monday, May 19, 2003 William H. Hsu Department of Computing and Information Sciences, KSU http://www.cis.ksu.edu/~bhsu Recommended Reading: KDD Intro, U. Fayyad Chapter 1, Machine Learning , T. M. MitchellBy kynan
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Integrated Data Mining Systems. Wei-Min Shen Information Sciences Institute University of Southern California. Outline. Objectives for Integrated System System Architecture Necessary Capabilities Representation Languages Actual System Descriptions. Objectives for Integrated KDD Systems.
Database Management Systems: Data Mining. Attribute Evaluation. Multiple Regression. Y = b 0 + b 1 X 1 + b 2 X 2 + … + b k X k. Regression estimates the b coefficients. If a b value is zero, the corresponding X attribute does not influence the Y variable.
MCMS Mining Course Management Systems. Samia Oussena Thames Valley University Samia.firstname.lastname@example.org. Project Aim. MCMS is a JISC funded project which aims to use data mining to support TVU strategy on students retention and course monitoring. Project Overview.
Database Management Systems: Data Mining. Market Baskets Association Rules. Association/Market Basket. Examples What items are customers likely to buy together? What Web pages are closely related? Others? Classic (early) example:
Literature Mining and Systems Biology. Lars Juhl Jensen EMBL. Why?. Overview. Information retrieval: finding the papers Entity recognition: identifying the substance(s) Information extraction: formalizing the facts Text mining: finding nuggets in the literature
Data Mining Systems and Languages. CS240A Notes. Knowledge Discovery (KDD) Process. Knowledge. Data mining—core of knowledge discovery process. Pattern Evaluation. Data Mining. Task-relevant Data. Selection. Data Warehouse. Data Cleaning. Data Integration. Databases.
Database Management Systems: Data Mining. Data Compression. The Need for Data Compression. Noisy data Need to combine data to smooth it (bins) Too many values in a dimension Need to combine data to get smaller number of sets Hierarchies Rollup data into natural hierarchies
Data Mining Algorithms for Recommendation Systems. Zhenglu Yang University of Tokyo. Sample Applications. Sample Applications. Corporate Intranets. Sample Applications. System Inputs. Interaction data (users items) Explicit feedback – rating, comments
Troubleshooting Distributed Systems via Data Mining. David Cieslak, Douglas Thain, and Nitesh Chawla University of Notre Dame. It’s Ugly in the Real World. Machine related failures: