Fuzzy learning models in expert systems
Fuzzy Sets and Systems - Special Double issue Fuzzy Set Theory in the USSR
Expert systems: knowledge, uncertainty, and decision
Expert systems: knowledge, uncertainty, and decision
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
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
Attribute weighting: a method of applying domain knowledge in the decision tree process
Proceedings of the seventh international conference on Information and knowledge management
A fuzzy inductive learning strategy for modular rules
Fuzzy Sets and Systems
Processing individual fuzzy attributes for fuzzy rule induction
Fuzzy Sets and Systems
A New Probabilistic Induction Method
Journal of Automated Reasoning
Object-Oriented Databases: Definition and Research Directions
IEEE Transactions on Knowledge and Data Engineering
Generalized Version Space Learning Algorithm for Noisy and Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
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
VL '95 Proceedings of the 11th International IEEE Symposium on Visual Languages
Mining Optimized Support Rules for Numeric Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
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Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Recently, the fuzzy and the object concepts have been very popular and used in a variety of applications, especially for complex data description. This paper thus proposes a new fuzzy data-mining algorithm for extracting interesting knowledge from quantitative transactions stored as object data. Each item itself is thought of as a class, and each item purchased in a transaction is thought of as an instance. Instances with the same class (item name) may have different quantitative attribute values since they may appear in different transactions. The proposed fuzzy algorithm can be divided into two main phases. The first phase is called the fuzzy intra-object mining phase, in which the linguistic large itemsets associated with the same classes (items) but with different attributes are derived. Each linguistic large itemset found in this phase is thought of as a composite item used in phase 2. The second phase is called the fuzzy inter-object mining phase, in which the large itemsets are derived and used to represent the relationship among different kinds of objects. An example is used to illustrate the algorithm. Experimental results are also given to show the effects of the proposed algorithm.