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
QuantMiner: a genetic algorithm for mining quantitative association rules
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Structuralization of universes
Fuzzy Sets and Systems
Mining association rules from semantic web data
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Using non boolean similarity functions for frequent similar pattern mining
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Mining frequent patterns and association rules using similarities
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
Frequent Pattern Mining is an important task due to the relevance of repetitions on data, also it is a fundamental step in the Association Rule Mining. Most of the current algorithms for mining frequent patterns assume that two object subdescriptions are similar if and only if they are equal, but in soft sciences some other similarity functions are used. In this work, we focus on the search of frequent patterns on Mixed Data, incorporating similarity between objects. We propose a novel and efficient algorithm to mine frequent similar patterns for a family of similarity functions that fulfill Downward Closure property and we also propose another algorithm for the remaining families of similarity functions. Some experiments over mixed datasets are done, and the results are compared against the ObjectMiner algorithm.