Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Mining fuzzy association rules in databases
ACM SIGMOD Record
Towards on-line analytical mining in large databases
ACM SIGMOD Record
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Online association rule mining
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
ACM Transactions on Information Systems (TOIS)
Efficient runtime generation of association rules
Proceedings of the tenth international conference on Information and knowledge management
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses
IEEE Transactions on Knowledge and Data Engineering
Modeling Multidimensional Databases
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Share Based Measures for Itemsets
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
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
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient Automated Mining of Fuzzy Association Rules
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
NetCube: A Scalable Tool for Fast Data Mining and Compression
Proceedings of the 27th International Conference on Very Large Data Bases
Mining Fuzzy Quantitative Association Rules
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Effective mining of fuzzy multi-cross-level weighted association rules
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
Mining fuzzy association rules in a bank-account database
IEEE Transactions on Fuzzy Systems
A New Fuzzy Multidimensional Model
IEEE Transactions on Fuzzy Systems
On a fuzzy group-by and its use for fuzzy association rule mining
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Building a highly-compact and accurate associative classifier
Applied Intelligence
PARAS: a parameter space framework for online association mining
Proceedings of the VLDB Endowment
FIRE: interactive visual support for parameter space-driven rule mining
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Inducing and storing generalised evidences using semantic web formalisms
HIKM '12 Proceedings of the Fifth Australasian Workshop on Health Informatics and Knowledge Management - Volume 129
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
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This paper addresses the integration of fuzziness with On-Line Analytical Processing (OLAP) based association rules mining. It contributes to the ongoing research on multidimensional online association rules mining by proposing a general architecture that utilizes a fuzzy data cube for knowledge discovery. A data cube is mainly constructed to provide users with the flexibility to view data from different perspectives as some dimensions of the cube contain multiple levels of abstraction. The first step of the process described in this paper involves introducing fuzzy data cube as a remedy to the problem of handling quantitative values of dimensional attributes in a cube. This facilitates the online mining of fuzzy association rules at different levels within the constructed fuzzy data cube. Then, we investigate combining the concepts of weight and multiple-level to mine fuzzy weighted multi-cross-level association rules from the constructed fuzzy data cube. For this purpose, three different methods are introduced for single dimension, multidimensional and hybrid (integrates the other two methods) fuzzy weighted association rules mining. Each of the three methods utilizes a fuzzy data cube constructed to suite the particular method. To the best of our knowledge, this is the first effort in this direction. We compared the proposed approach to an existing approach that does not utilize fuzziness. Experimental results obtained for each of the three methods on a synthetic dataset and on the adult data of the United States census in year 2000 demonstrate the effectiveness and applicability of the proposed fuzzy OLAP based mining approach.