• Publications
  • Influence
Discovery of Multiple-Level Association Rules from Large Databases
TLDR
In this paper, a top-down progressive deepening method is developed for mining multiplelevel association rules from large transaction databases by extension of some existing association rule mining techniques. Expand
  • 1,161
  • 55
  • PDF
A fast distributed algorithm for mining association rules
TLDR
We propose a fast distributed mining algorithm, FDM, which generates a small number of candidate sets and substantially reduces the number of messages to be passed at mining association rules. Expand
  • 528
  • 33
  • PDF
Mining Multiple-Level Association Rules in Large Databases
  • Jiawei Han, Y. Fu
  • Computer Science
  • IEEE Trans. Knowl. Data Eng.
  • 1 September 1999
TLDR
A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the a priori principle. Expand
  • 361
  • 26
  • PDF
Efficient Mining of Association Rules in Distributed Databases
TLDR
An efficient algorithm called DMA (Distributed Mining of Association rules), is proposed. Expand
  • 395
  • 12
  • PDF
DMQL: A Data Mining Query Language for Relational Databases
TLDR
We design a data mining query language, DMQL, for mining di erent kinds of knowledge in relational databases. Expand
  • 219
  • 12
  • PDF
DBMiner: A System for Mining Knowledge in Large Relational Databases
TLDR
A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases. Expand
  • 194
  • 11
  • PDF
Exploration of the power of attribute-oriented induction in data mining
TLDR
Attribute-oriented induction is a set-oriented database mining method which generalizes the task-relevant subset of data attribute-by-attribute, compresses it into a generalized relation, and extracts from it the general features of data. Expand
  • 102
  • 6
Clustering of Web Users Based on Access Patterns
  • 139
  • 5
Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases
TLDR
Some algorithms are developed for automatic generation of concept hierarchies for numerical attributes based on data distributions and for dynamic refinement of a given or generated concept hierarchy based on a learning request, the relevant set of data and database statistics. Expand
  • 206
  • 4
  • PDF
A Generalization-Based Approach to Clustering of Web Usage Sessions
TLDR
The clustering of Web usage sessions based on the access patterns is studied. Expand
  • 152
  • 4