Efficient estimation of dynamic density functions with an application to outlier detection

Abdulhakim Ali Qahtan, Xiangliang Zhang, Suojin Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages2159-2163
Number of pages5
DOIs
Publication statusPublished - 19 Dec 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: 29 Oct 20122 Nov 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period29/10/122/11/12

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Keywords

  • data streams
  • density estimation
  • interpolation
  • outlier detection

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Qahtan, A. A., Zhang, X., & Wang, S. (2012). Efficient estimation of dynamic density functions with an application to outlier detection. In CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management (pp. 2159-2163). (ACM International Conference Proceeding Series). https://doi.org/10.1145/2396761.2398593