Knowledge Base about the database industry: database software, database management systems, database application development, data warehouse, data mining, business intelligence, OLAP, database design, data modeling, data integration, database security.
Certain type of database application that process and analysis large datasets or databases looking for patterns, usually to build forecasting models for decision making managers. Other definitions are “The nontrivial extraction of implicit, previously unknown, and potentially useful information from data” (1) or “The science of extracting useful information from large data sets or databases” (2). Also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from statistics, machine learning and pattern recognition.
A classical example of data mining is its use in retail sales departments. If a store tracks the purchases of a customer and notices that a customer buys a lot of silk ties, the data mining system will make a correlation between that customer and silk ties. The sales department will look at that information and may begin direct mail marketing of silk ties to that customer, or it may alternatively attempt to get the customer to buy a wider range of products. In this case, the data mining system used by the retail store discovered new information about the customer that was previously unknown to the company. Another widely used hypothetical example is that of a large North American chain of supermarkets. Through intensive analysis of the transactions and the goods bought over a period of time, i.e. data mining, analysts found that beers and diapers were often bought together. Though explaining this interrelation might be difficult, taking advantage of it, on the other hand, should not be hard (e.g. placing the high-profit diapers next to the high-profit beers). This technique is often referred to as "Market Basket Analysis".
(1) W. Frawley and G. Piatetsky-Shapiro and C. Matheus, Knowledge Discovery in Databases: An Overview. AI Magazine, Fall 1992, pp. 213-228.
(2) D. Hand, H. Mannila, P. Smyth: Principles of Data Mining. MIT Press, Cambridge, MA, 2001. ISBN 0-262-08290-X
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The University of Washington's database group is focused on broadening the scope of database and data management techniques beyond their traditional scope. We do both theoretical and systems work in areas such as databases and the web, XML, data management for ubiquitous computing, data integration, and data mining. We also frequently work with the UW Artificial Intelligence, Intelligent Information Systems, Distributed Systems, and Ubiquitous Computing groups.
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A collection of Data Mining links edited by the Central Connecticut State University
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Magazine covering Data Mining, Business Inteligence, Data Integration, Data Analisys and more.
