TY - GEN
T1 - Evolution of interesting association rules online with learning classifier systems
AU - Orriols-Puig, Albert
AU - Casillas, Jorge
PY - 2010
Y1 - 2010
N2 - This paper presents CSar, aMichigan-style learning classifier system designed to extract quantitative association rules from streams of unlabeled examples. The main novelty of CSar with respect to the existing association rule miners is that it evolves the knowledge online and it is thus prepared to adapt its knowledge to changes in the variable associations hidden in the stream of unlabeled data quickly and efficiently. The results provided in this paper show that CSar is able to evolve interesting rules on problems that consist of both categorical and continuous attributes. Moreover, the comparison of CSar with Apriori on a problem that consists only of categorical attributes highlights the competitiveness of CSar with respect to more specific learners that perform enumeration to return all possible association rules. These promising results encourage us to further investigate on CSar.
AB - This paper presents CSar, aMichigan-style learning classifier system designed to extract quantitative association rules from streams of unlabeled examples. The main novelty of CSar with respect to the existing association rule miners is that it evolves the knowledge online and it is thus prepared to adapt its knowledge to changes in the variable associations hidden in the stream of unlabeled data quickly and efficiently. The results provided in this paper show that CSar is able to evolve interesting rules on problems that consist of both categorical and continuous attributes. Moreover, the comparison of CSar with Apriori on a problem that consists only of categorical attributes highlights the competitiveness of CSar with respect to more specific learners that perform enumeration to return all possible association rules. These promising results encourage us to further investigate on CSar.
UR - http://www.scopus.com/inward/record.url?scp=79956335576&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17508-4_2
DO - 10.1007/978-3-642-17508-4_2
M3 - Conference contribution
AN - SCOPUS:79956335576
SN - 3642175074
SN - 9783642175077
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 21
EP - 37
BT - Learning Classifier Systems - 11th International Workshop, IWLCS 2008 and 12th International Workshop, IWLCS 2009, Revised Selected Papers
T2 - 11th International Workshop on Learning Classifier Systems, IWLCS 2008 and 12th International Workshop on Learning Classifier Systems, IWLCS 2009
Y2 - 9 July 2009 through 9 July 2009
ER -