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Using concept lattices as a theoretical background for finding association rules [11] has led to designing algorithms like Charm [10], Close [7] or Closet [8]. While they are considered as extremely appropriate when finding concepts for association rules, due to the smaller amount of results, they do not cover a certain area of significant results, namely(More)
INTRODUCTION Association rules, introduced by Agrawal, Imielinski and Swami (1993), provide useful means to discover associations in data. The problem of mining association rules in a database is defined as finding all the association rules that hold with more than a user-given minimum support threshold and a user-given minimum confidence threshold.(More)
There are several approaches in trying to solve the Quantitative Structure-Activity (QSAR) problem. These approaches are based either on statistical methods or on predictive data mining using neural networks. Among the statistical methods, one should consider regression analysis, pattern recognition (such as cluster analysis, factor analysis and principal(More)
Fuzzy selection criteria querying relational databases include vague terms; they usually refer linguistic values form the attribute linguistic domains, defined as fuzzy sets. Generally, when a vague query is processed, the definitions of vague terms must already exist in a knowledge base. But there are also cases when vague terms must be dynamically(More)