Knowledge Mining in Databases: An Integration of Machine Learning Methodologies with Database Techno

Active research has been conducted on knowledge discovery in databases by the researchers in our group for years, with many interesting results published and a prototyped knowledge discovery system, DBMiner (previously called DBLearn), developed and demonstrated in several conferences. Our research covers a wide spectrum of knowledge discovery, including (1) the study of knowledge discovery in relational, object-oriented, deductive, spatial, and active databases, and global information systems, and (2) the development of various kinds of knowledge discovery methods, including attribute-oriented induction, progressive deepening for mining multiple-level rules, meta-rule guided knowledge mining, etc. Techniques for the discovery of various kinds of knowledge, including generalization, characterization, discrimination, association, classi cation, clustering, etc. and the application of knowledge discovery for intelligent query answering, multiple-layered database construction, etc. have also been studied in our research.

[1]  Ryszard S. Michalski,et al.  A theory and methodology of inductive learning , 1993 .

[2]  Jiawei Han,et al.  DBLearn: a system prototype for knowledge discovery in relational databases , 1994, SIGMOD '94.

[3]  Ryszard S. Michalski,et al.  Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[5]  Jiawei Han,et al.  Advances of the DBLearn System for Knowledge Discovery in Large Databases , 1995, IJCAI.

[6]  Hans-Peter Kriegel,et al.  Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification , 1995, SSD.

[7]  Jiawei Han,et al.  Resource and Knowledge Discovery in Global Information Systems: A Preliminary Design and Experiment , 1995, KDD.

[8]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[9]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

[10]  Jiawei Han,et al.  Exploration of the power of attribute-oriented induction in data mining , 1995, KDD 1995.

[11]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[12]  J I Brauman,et al.  Computing in science. , 1993, Science.

[13]  Jiawei Han,et al.  Intelligent Query Answering by Knowledge Discovery Techniques , 1996, IEEE Trans. Knowl. Data Eng..

[14]  Jiawei Han,et al.  Knowledge Discovery in Databases: An Attribute-Oriented Approach , 1992, VLDB.

[15]  Jiawei Han,et al.  Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases , 1994, KDD Workshop.

[16]  Jiawei Han,et al.  Cooperative Query Answering Using Multiple Layered Databases , 1994, CoopIS.

[17]  R. Ng,et al.  Eecient and Eeective Clustering Methods for Spatial Data Mining , 1994 .

[18]  Jiawei Han,et al.  Meta-Rule-Guided Mining of Association Rules in Relational Databases , 1995, KDOOD/TDOOD.

[19]  Jiawei Han,et al.  Data-Driven Discovery of Quantitative Rules in Relational Databases , 1993, IEEE Trans. Knowl. Data Eng..

[20]  Jiawei Han.  Knowledge Discovery in Object-Oriented and Active Databases , 1993 .

[21]  Jiawei Han,et al.  Discovery of Spatial Association Rules in Geographic Information Databases , 1995, SSD.