A method for mining quantitative association rules

Authors:
María N. Moreno;Saddys Segrera;Vivian F. López;M. José Polo
Affiliations:
Department of Computing and Automatic, University of Salamanca, Salamanca, Spain;Department of Computing and Automatic, University of Salamanca, Salamanca, Spain;Department of Computing and Automatic, University of Salamanca, Salamanca, Spain;Department of Computing and Automatic, University of Salamanca, Salamanca, Spain
Venue:
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Year:
2006

Citing 14
Cited 1

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Unexpectedness as a measure of interestingness in knowledge discovery

Decision Support Systems - Special issue on WITS '97
Knowledge refinement based on the discovery of unexpected patterns in data mining

Decision Support Systems - Special issue: Formal modeling and electronic commerce
Techniques of Cluster Algorithms in Data Mining

Data Mining and Knowledge Discovery
Analyzing the Subjective Interestingness of Association Rules

IEEE Intelligent Systems
Database Mining: A Performance Perspective

IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules Using Pruning Techniques

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Tree Structures for Mining Association Rules

Data Mining and Knowledge Discovery
Mining Non-Redundant Association Rules

Data Mining and Knowledge Discovery
Building knowledge discovery-driven models for decision support in project management

Decision Support Systems

Transaction-item association matrix-based frequent pattern network mining algorithm in large-scale transaction database

WSEAS Transactions on Computers

Quantified Score

Hi-index	0.00

Visualization

Abstract

Association rule mining is a significant research topic in the knowledge discovery area. In the last years a great number of algorithms have been proposed with the objective of solving diverse drawbacks presented in the generation of association rules. One of the main problems is to obtain interesting rules from continuous numeric attributes. In this paper, a method for mining quantitative association rules is proposed. It deals with the problem of discretizing continuous data in order to discover a manageable number of high confident association rules, which cover a high percentage of examples in the data set. The method was validated by applying it to data from software project management metrics.