LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
M-Kernel Merging: Towards Density Estimation over Data Streams
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Automatic outlier detection for time series: an application to sensor data
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Malicious Node Detection in Wireless Sensor Networks Using an Autoregression Technique
ICNS '07 Proceedings of the Third International Conference on Networking and Services
Cluster Kernels: Resource-Aware Kernel Density Estimators over Streaming Data
IEEE Transactions on Knowledge and Data Engineering
DBOD-DS: distance based outlier detection for data
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
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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.