Foundations and Advances in Data Mining

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Chu, Wesley (Επιμελητής έκδοσης), Lin, Tsau Young (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.
• Σειρά: Studies in Fuzziness and Soft Computing, 180
• Θέματα: Computer science.; Computers.; Artificial intelligence.; Information theory.; Applied mathematics.; Engineering mathematics.; Computer Science.; Theory of Computation.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).; Information Systems and Communication Service.; Information and Communication, Circuits.

 With the growing use of information technology and the recent advances in web systems, the amount of data available to users has increased exponentially. Thus, there is a critical need to understand the content of the data. As a result, data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. In this carefully edited volume a theoretical foundation as well as important new directions for data-mining research are presented. It brings together a set of well respected data mining theoreticians and researchers with practical data mining experiences. The presented theories will give data mining practitioners a scientific perspective in data mining and thus provide more insight into their problems, and the provided new data mining topics can be expected to stimulate further research in these important directions. 

 The Mathematics of Learning -- Logical Regression Analysis: From Mathematical Formulas to Linguistic Rules -- A Feature/Attribute Theory for Association Mining and Constructing the Complete Feature Set -- A New Theoretical Framework for K-means-type Clustering -- Clustering via Decision Tree Construction -- Incremental Mining on Association Rules -- Mining Association Rules from Tabular Data Guided by Maximal Frequent Itemsets -- Sequential Pattern Mining by Pattern-Growth: Principles and Extensions -- Web Page Classification -- Web Mining – Concepts, Applications, and Research Directions -- Privacy-Preserving Data Mining.