V. Torra (Ed.)
Information Fusion in Data Mining
Studies in Fuzziness and Soft Computing, Vol. 123
Springer-Verlag, Heidelberg, ISBN 3-540-00676-1
Information fusion is becoming a major need
in data mining and knowledge discovery in
databases. In this book, fusion techniques
that are currently in use in data mining, as
well as data mining applications that use
information fusion are presented.

Contents:

Trends in Information Fusion in Data Mining, V. Torra

Part 1. Aggregation Operators: Methods and Properties

On some aggregation operators for numerical information, V. Torra; Choquet integral and Sugeno integral as aggregation functions, Y. Narukawa, T. Murofushi; Data Mining Using a Probabilistic Weighted Ordered Weighted Average (PWOWA) Operator, H. B. Mitchell

Part 2. Preprocessing Data

Mining Interesting Patterns in Multiple Data Sources, N. Zhong; Discovery of Temporal Knowledge in Medical Time-Series Databases using Moving Average, Multiscale Matching and Rule Induction, S. Tsumoto; Record linkage methods for multidatabase data mining, V. Torra, J. Domingo-Ferrer

Part 3. Model Building

Modelling data by Choquet integral, M. Grabisch; An Algorithm Based on Alternative Projections for a Fuzzy Measure Identification Problem, H. Imai, D. Asano, Y. Sato; Combining Information Fusion with String Pattern Analysis: A New Method for Predicting Future Purchase Behavior, Y. Hamuro, N. Katoh, E. H. Ip, S. L. Cheung, K. Yada; Ensemble Learning by a Fuzzy Classification Systems for Pattern Classification, T. Nakashima, G. Nakai

Part 4. Information Extraction

Data Mining Using Granular Linguistic Summaries, R. R. Yager

Part 4. Information Extraction

Data Mining Using Granular Linguistic Summaries, R. R. Yager

Comprehensive subject index