KDE-Track: An Efficient Dynamic Density Estimator for Data Streams
Type
ArticleKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2016-11-08Online Publication Date
2016-11-08Print Publication Date
2017-03-01Permanent link to this record
http://hdl.handle.net/10754/621861
Metadata
Show full item recordAbstract
Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. The construction of kernel density estimators is well studied and documented. However, existing techniques are either expensive or inaccurate and unable to capture the changes in the data distribution. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream. We also propose an accurate and efficient method for selecting the bandwidth value for the kernel density estimator, which increases its accuracy significantly. Both theoretical analysis and experimental validation show that KDE-Track outperforms a set of baseline methods on the estimation accuracy and computing time of complex density structures in data streams.Citation
Qahtan A, Wang S, Zhang X (2016) KDE-Track: An Efficient Dynamic Density Estimator for Data Streams. IEEE Transactions on Knowledge and Data Engineering: 1–1. Available: http://dx.doi.org/10.1109/TKDE.2016.2626441.Additional Links
http://ieeexplore.ieee.org/document/7738463/ae974a485f413a2113503eed53cd6c53
10.1109/TKDE.2016.2626441