Adaptive fuzzy partitions for evolving association rules in big data stream
作者： Elena Ruiz , Jorge Casillas , None
DOI: 10.1016/J.IJAR.2017.11.014
关键词:
摘要: Abstract The amount of data being generated in industrial and scientific applications is constantly increasing. These are often as a chronologically ordered unlabeled flow which exceeds usual storage processing capacities. Association stream mining an appealing field models complex environments online by finding relationships among the attributes without presupposing any priori structure. discovered continuously adapted to dynamics problem pure way, able deal with both categorical continuous attributes. This paper presents new advanced version, Fuzzy-CSar-AFP, genetic fuzzy system designed obtain interesting association rules from streams. It capable managing partitions different granularity for variables, allows algorithm adapt precision requirements each variable rule. can also work streams needing know domains it includes mechanism updates them real-time. Fuzzy-CSar-AFP performance validated original real-world Psychophysiology where associations between electroencephalogram signals subjects put through stimuli analyzed.
sci-hub.se 参考文章(45)
Plamen P. Angelov, Autonomous Learning Systems: From Data Streams to Knowledge in Real-time ,(2013)
Daniela Gorski Trevisan, Nayat Sanchez-Pi, Luis Marti, Ana Cristina Bicharra Garcia, Big Data Visualization for Occupational Health and Security Problem in Oil and Gas Industry Human Interface and the Management of Information. Information and Knowledge Design. pp. 46- 54 ,(2015) , 10.1007/978-3-319-20612-7_5
Shaheen, M., Rehman, Z., Shahbaz, M., Guergachi, A., FPS-Tree Algorithm to Find Top-k Closed Itemsets in Data Streams Arabian Journal for Science and Engineering. ,vol. 40, pp. 3507- 3521 ,(2015) , 10.1007/S13369-015-1811-X
Bartosz Krawczyk, Michal Wozniak, Reacting to different types of concept drift with adaptive and incremental one-class classifiers 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF). pp. 30- 35 ,(2015) , 10.1109/CYBCONF.2015.7175902
Jacinto Mata, José-Luis Alvarez, José-Cristobal Riquelme, Discovering Numeric Association Rules via Evolutionary Algorithm knowledge discovery and data mining. pp. 40- 51 ,(2002) , 10.1007/3-540-47887-6_5
Michelle W. Voss, Carmen Vivar, Arthur F. Kramer, Henriette van Praag, Bridging animal and human models of exercise-induced brain plasticity. Trends in Cognitive Sciences. ,vol. 17, pp. 525- 544 ,(2013) , 10.1016/J.TICS.2013.08.001
Hua-Fu Li, Man-Kwan Shan, Suh-Yin Lee, DSM-FI: an efficient algorithm for mining frequent itemsets in data streams Knowledge and Information Systems. ,vol. 17, pp. 79- 97 ,(2008) , 10.1007/S10115-007-0112-4
Zahra Farzanyar, Mohammadreza Kangavari, Nick Cercone, Max-FISM: Mining (recently) maximal frequent itemsets over data streams using the sliding window model Computers & Mathematics With Applications. ,vol. 64, pp. 1706- 1718 ,(2012) , 10.1016/J.CAMWA.2012.01.045
B. P. Welford, Note on a Method for Calculating Corrected Sums of Squares and Products Technometrics. ,vol. 4, pp. 419- 420 ,(1962) , 10.1080/00401706.1962.10490022
W. Nick Street, YongSeog Kim, A streaming ensemble algorithm (SEA) for large-scale classification knowledge discovery and data mining. pp. 377- 382 ,(2001) , 10.1145/502512.502568