Inductive Databases and Constraint-Based Data Mining by Sašo Džeroski (auth.), Sašo Džeroski, Bart Goethals, Panče

By Sašo Džeroski (auth.), Sašo Džeroski, Bart Goethals, Panče Panov (eds.)

This publication is ready inductive databases and constraint-based information mining, rising study subject matters mendacity on the intersection of information mining and database learn. the purpose of the e-book as to supply an summary of the state-of- the artwork during this novel and - mentioning study region. Of targeted curiosity are the hot equipment for constraint-based mining of world types for prediction and clustering, the uni?cation of trend mining methods via constraint programming, the clari?cation of the re- tionship among mining neighborhood styles and worldwide types, and the proposed in- grative frameworks and ways for inducive databases. at the software part, functions to essentially appropriate difficulties from bioinformatics are offered. Inductive databases (IDBs) symbolize a database view on info mining and kno- part discovery. IDBs include not just facts, but additionally generalizations (patterns and versions) legitimate within the information. In an IDB, usual queries can be utilized to entry and - nipulate facts, whereas inductive queries can be utilized to generate (mine), manage, and practice styles and types. within the IDB framework, styles and versions turn into ”?rst-class voters” and KDD turns into a longer querying technique within which either the knowledge and the patterns/models that carry within the information are queried.

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The formal definition of experiments for analysis, annotation and sharing of results is a fundamental part of scientific practice. A generic ontology of experiments EXPO [45] tries to define the principal entities for representation of scientific investigations. EXPO defines types of investigations: EXPO:computational investigation, EXPO:physical investigation and their principal components: EXPO:investigator, EXPO:method, EXPO:result, EXPO:conclusion. The EXPO ontology is of a general value in describing experiments from various areas of research.

Free-sets: a condensed representation of boolean data for the approximation of frequency queries. Data Mining and Knowledge Discovery, 7(1):5–22. 7. -F. Boulicaut, L. De Raedt, and H. Mannila, editors (2005). Constraint-Based Mining and Inductive Databases. Springer, Berlin. 8. J-F. Boulicaut and B. Jeudy (2005). Constraint-based data mining. In O. Maimon and L. Rokach, editors, The Data Mining and Knowledge Discovery Handbook, pages 399–416. Springer, Berlin. 9. -F. Boulicaut, M. Klemettinen, and H.

In S. Basu, I. Davidson, and K. Wagstaff, editors, Constrained Clustering: Advances in Algorithms, Theory and Applications, pages 145–170. Chapman & Hall/CRC Press, Boca Raton, FL. 35. K. Wagstaff and C. Cardie (2000). Clustering with instance-level constraints. In Proc. 17th Intl. Conf. on Machine Learning, pages 1103–1110. Morgan Kaufmann, San Francisco, CA. 36. Q. Yang and X. Wu (2006). 10 Challenging problems in data mining research. International Journal of Information Technology & Decision Making, 5(4): 597–604.

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