An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
GeneScout: a data mining system for predicting vertebrate genes in genomic DNA sequences
Information Sciences: an International Journal - Special issue: Soft computing data mining
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Hierarchical clustering of mixed data based on distance hierarchy
Information Sciences: an International Journal
Calibrated lazy associative classification
Information Sciences: an International Journal
Information Sciences: an International Journal
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Speed-density relationships are used by mesoscopic traffic simulators to represent traffic dynamics. While classical speed-density relationships provide useful insights into the traffic dynamics problem, they may be restrictive for such applications. This paper addresses the problem of calibrating speed-density relationship parameters using data mining techniques, and proposes a novel hierarchical clustering algorithm based on K-means clustering. By combining K-means with agglomerative hierarchical clustering, the proposed new algorithm is able to reduce early-stage errors inherent in agglomerative hierarchical clustering resulted in improved clustering performance. Moreover, in order to improve the precision of parametric calibration, densities and flows are utilized as variables. The proposed approach is tested against sensor data captured from the 3rd Ring Road of Beijing. The testing results show that the performance of our algorithm is better than existing solutions.