TY - GEN
T1 - Fuzzy association rule mining from spatio-temporal data
AU - Calargun, Seda Unal
AU - Yazici, Adnan
N1 - Funding Information:
This research is partially supported by TUBITAK in the project with number 106E012.
PY - 2008
Y1 - 2008
N2 - The use of fuzzy sets in mining association rules from spatio-temporal databases is useful since fuzzy sets are able to model the uncertainty embedded in the meaning of data. There are several fuzzy association rule mining techniques that can work on spatio-temporal data. Their ability to mine fuzzy association rules has to be compared on a realistic scenario. Besides the performance criteria, other criteria that can express the quality of an association rule discovered shall be specified. In this paper, fuzzy association rule mining is performed with spatio-temporal data cubes and Apriori algorithm. A real life application is developed to compare data cubes and Apriori algorithm according to the following criteria: interpretability, precision, utility, novelty, direct-to-the-point, performance and visualization, which are defined within the scope of this paper.
AB - The use of fuzzy sets in mining association rules from spatio-temporal databases is useful since fuzzy sets are able to model the uncertainty embedded in the meaning of data. There are several fuzzy association rule mining techniques that can work on spatio-temporal data. Their ability to mine fuzzy association rules has to be compared on a realistic scenario. Besides the performance criteria, other criteria that can express the quality of an association rule discovered shall be specified. In this paper, fuzzy association rule mining is performed with spatio-temporal data cubes and Apriori algorithm. A real life application is developed to compare data cubes and Apriori algorithm according to the following criteria: interpretability, precision, utility, novelty, direct-to-the-point, performance and visualization, which are defined within the scope of this paper.
KW - Association rule mining
KW - Association rule mining comparison criteria
KW - Data mining
KW - Fuzzy association rules
KW - Fuzzy spatio-temporal data cube
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U2 - 10.1007/978-3-540-69839-5_47
DO - 10.1007/978-3-540-69839-5_47
M3 - Conference contribution
AN - SCOPUS:54249149374
SN - 3540698388
SN - 9783540698388
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 631
EP - 646
BT - Computational Science and Its Applications - ICCSA 2008 - International Conference, Proceedings
T2 - International Conference on Computational Science and Its Applications, ICCSA 2008
Y2 - 30 June 2008 through 3 July 2008
ER -