期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 54, 期 2, 页码 463-486出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-017-1067-8
关键词
Anomaly detection; Time series; DTW; Weighted clustering
In the last decade, outlier detection for temporal data has received much attention from data mining and machine learning communities. While other works have addressed this problem by two-way approaches (similarity and clustering), we propose in this paper an embedded technique dealing with both methods simultaneously. We reformulate the task of outlier detection as a weighted clustering problem based on entropy and dynamic time warping for time series. The outliers are then detected by an optimization problem of a new proposed cost function adapted to this kind of data. Finally, we provide some experimental results for validating our proposal and comparing it with other methods of detection.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据