Statistical analysis with missing data
Statistical analysis with missing data
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 Bases for Association Rules Using Closed Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
SKIF: a data imputation framework for concept drifting data streams
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Imputing missing values in nuclear safeguards evaluation by a 2-tuple computational model
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
A sliding window-based false-negative approach for ubiquitous data stream analysis
International Journal of Communication Systems
Mining of multiobjective non-redundant association rules in data streams
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Dispersion-based prediction framework for estimating missing values in wireless sensor networks
International Journal of Sensor Networks
A data imputation model in sensor databases
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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Networks of thousands of sensors present a feasible and economic solution to some of our most challenging problems, such as real-time traffic modeling, military sensing and tracking. Many research projects have been conducted by different organizations regarding wireless sensor networks; however, few of them discuss how to estimate missing sensor data. In this research we present a novel data estimation technique based on association rules derived from closed frequent itemsets generated by sensors. Experimental results compared with the existing techniques using real-life sensor data show that closed itemset mining effectively imputes missing values as well as achieves time and space efficiency.