Efficient estimation of dynamic density functions with an application to outlier detection
Type
Conference PaperKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Machine Intelligence & kNowledge Engineering Lab
Date
2012Permanent link to this record
http://hdl.handle.net/10754/564486
Metadata
Show full item recordAbstract
In this paper, we propose a new method to estimate the dynamic density over data streams, named KDE-Track as it is based on a conventional and widely used Kernel Density Estimation (KDE) method. KDE-Track can efficiently estimate the density with linear complexity by using interpolation on a kernel model, which is incrementally updated upon the arrival of streaming data. Both theoretical analysis and experimental validation show that KDE-Track outperforms traditional KDE and a baseline method Cluster-Kernels on estimation accuracy of the complex density structures in data streams, computing time and memory usage. KDE-Track is also demonstrated on timely catching the dynamic density of synthetic and real-world data. In addition, KDE-Track is used to accurately detect outliers in sensor data and compared with two existing methods developed for detecting outliers and cleaning sensor data. © 2012 ACM.Conference/Event name
21st ACM International Conference on Information and Knowledge Management, CIKM 2012ISBN
9781450311564ae974a485f413a2113503eed53cd6c53
10.1145/2396761.2398593