An inductive learning procedure to identify fuzzy systems
Fuzzy Sets and Systems
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Learning rules for a fuzzy inference model
Fuzzy Sets and Systems - Special issue on fuzzy data analysis
Induction of fuzzy decision trees
Fuzzy Sets and Systems
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
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A fuzzy inductive learning strategy for modular rules
Fuzzy Sets and Systems
Processing individual fuzzy attributes for fuzzy rule induction
Fuzzy Sets and Systems
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Itemsets Using Support Constraints
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Optimized Support Rules for Numeric Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining association rules with multiple minimum supports using maximum constraints
International Journal of Approximate Reasoning
A new logic correlation rule for HIV-1 protease mutation
Expert Systems with Applications: An International Journal
Mining generalized association rules with quantitative data under multiple support constraints
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Strategic decisions using the fuzzy PROMETHEE for IS outsourcing
Expert Systems with Applications: An International Journal
New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports
Journal of Systems and Software
Exploring fuzzy ontologies in mining generalized association rules
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Information Sciences: an International Journal
Review: Recent developments in the organization goals conformance using ontology
Expert Systems with Applications: An International Journal
A fuzzy coherent rule mining algorithm
Applied Soft Computing
Discovering frequent itemsets on uncertain data: a systematic review
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
MOGA-based fuzzy data mining with taxonomy
Knowledge-Based Systems
Using data mining synergies for evaluating criteria at pre-qualification stage of supplier selection
Journal of Intelligent Manufacturing
Hi-index | 12.06 |
Finding association rules in transaction databases is most commonly seen in data mining. In real applications, different items may have different support criteria to judge their importance, taxonomic relationships among items may appear, and data may have quantitative values. This paper thus proposes a fuzzy multiple-level mining algorithm for extracting knowledge implicit in quantitative transactions with multiple minimum supports of items. Items may have different minimum supports and the maximum-itemset minimum-taxonomy support constraint is adopted to discover the large itemsets. Under the constraint, the characteristic of downward-closure is kept, such that the original apriori algorithm can be easily extended to find fuzzy large itemsets. The proposed algorithm adopts a top-down progressively deepening approach to derive large itemsets. It can also discover cross-level fuzzy association rules under the maximum-itemset minimum-taxonomy support constraint. An example is also given to demonstrate that the proposed mining algorithm can derive the multiple-level association rules under multiple item supports in a simple and effective way.