Towards on-line analytical mining in large databases

Great efforts have been paid in the Intelligent Database Systems Research Lab for the research and development of efficient data mining methods and construction of on-line analytical data mining systems.Our work has been focused on the integration of data mining and OLAP technologies and the development of scalable, integrated, and multiple data mining functions. A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of data mining functions, including characterization, comparison, association, classification, prediction, and clustering. It also builds up a user-friendly, interactive data mining environment and a set of knowledge visualization tools. In-depth research has been performed on the efficiency and scalability of data mining methods. Moreover, the research has been extended to spatial data mining, multimedia data mining, text mining, and Web mining with several new data mining system prototypes constructed or under construction, including GeoMiner, MultiMediaMiner, and WebLogMiner.This article summarizes our research and development activities in the last several years and shares our experiences and lessons with the readers.

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

[2]  Jorma Rissanen,et al.  SLIQ: A Fast Scalable Classifier for Data Mining , 1996, EDBT.

[3]  Jiawei Han,et al.  Maintenance of discovered association rules in large databases: an incremental updating technique , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[4]  Wan Gong.  Periodic pattern search on time-related data sets , 1997 .

[5]  Gang Liu,et al.  DBMiner: a system for data mining in relational databases and data warehouses , 1997, CASCON.

[6]  Kyuseok Shim,et al.  Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases , 1995, VLDB.

[7]  Jiawei Han,et al.  A fast distributed algorithm for mining association rules , 1996, Fourth International Conference on Parallel and Distributed Information Systems.

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

[9]  Jiawei Han,et al.  Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs , 1998, Proceedings IEEE International Forum on Research and Technology Advances in Digital Libraries -ADL'98-.

[10]  Jiawei Han,et al.  Selective Materialization: An Efficient Method for Spatial Data Cube Construction , 1998, PAKDD.

[11]  Jeffrey F. Naughton,et al.  On the Computation of Multidimensional Aggregates , 1996, VLDB.

[12]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[13]  Betty Bin Xia,et al.  Similarity search in time series data sets , 1997 .

[14]  Jiawei Han,et al.  Generalization and decision tree induction: efficient classification in data mining , 1997, Proceedings Seventh International Workshop on Research Issues in Data Engineering. High Performance Database Management for Large-Scale Applications.

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

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

[17]  Surajit Chaudhuri,et al.  An overview of data warehousing and OLAP technology , 1997, SGMD.

[18]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[19]  Laks V. S. Lakshmanan,et al.  Exploratory mining and pruning optimizations of constrained associations rules , 1998, SIGMOD '98.

[20]  Jiawei Han,et al.  Attribute-Oriented Induction in Relational Databases , 1991, Knowledge Discovery in Databases.

[21]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

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

[23]  Jiawei Han,et al.  OLAP Mining: Integration of OLAP with Data Mining , 1997, DS-7.

[24]  Jiawei Han,et al.  Attribute-Oriented Induction in data Mining , 1996, Advances in Knowledge Discovery and Data Mining.

[25]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[26]  Jeffrey F. Naughton,et al.  An array-based algorithm for simultaneous multidimensional aggregates , 1997, SIGMOD '97.

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

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

[29]  Jiawei Han,et al.  Generalization-Based Data Mining in Object-Oriented Databases Using an Object Cube Model , 1998, Data Knowl. Eng..

[30]  Jiawei Han,et al.  Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes , 1997, KDD.