Two-Step Anomaly Detection Approach Using Clustering Algorithm

2018 ◽  
pp. 513-520 ◽  
Author(s):  
Praphula Kumar Jain ◽  
Rajendra Pamula
Keyword(s):  
Anomaly Detection ◽  
Clustering Algorithm ◽  
Detection Approach

Efficient mixed clustering algorithm and its application in anomaly detection

2010 ◽  
Vol 30 (7) ◽  
pp. 1916-1918 ◽  
Author(s):  
Jian-guo LI ◽  
Xue-gang HU
Keyword(s):  
Anomaly Detection ◽  
Clustering Algorithm

scholarly journals Random Collective Representation-Based Detector with Multiple Features for Hyperspectral Images

2021 ◽  
Vol 13 (4) ◽  
pp. 721
Author(s):  
Zhongheng Li ◽  
Fang He ◽  
Haojie Hu ◽  
Fei Wang ◽  
Weizhong Yu
Keyword(s):  
Anomaly Detection ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Collaborative Representation ◽  
Detection Accuracy ◽  
Spatial Features ◽  
Collective Representation ◽  
Detection Approach ◽  
Adaptive Weight ◽  
Gabor Feature

Collaborative representation-based detector (CRD), as the most representative anomaly detection method, has been widely applied in the field of hyperspectral anomaly detection (HAD). However, the sliding dual window of the original CRD introduces high computational complexity. Moreover, most HAD models only consider a single spectral or spatial feature of the hyperspectral image (HSI), which is unhelpful for improving detection accuracy. To solve these problems, in terms of speed and accuracy, we propose a novel anomaly detection approach, named Random Collective Representation-based Detector with Multiple Feature (RCRDMF). This method includes the following steps. This method first extract the different features include spectral feature, Gabor feature, extended multiattribute profile (EMAP) feature, and extended morphological profile (EMP) feature matrix from the HSI image, which enables us to improve the accuracy of HAD by combining the multiple spectral and spatial features. The ensemble and random collaborative representation detector (ERCRD) method is then applied, which can improve the anomaly detection speed. Finally, an adaptive weight approach is proposed to calculate the weight for each feature. Experimental results on six hyperspectral datasets demonstrate that the proposed approach has the superiority over accuracy and speed.

scholarly journals Anomaly detection in multi-class time series

2021 ◽  
Vol 2113 (1) ◽  
pp. 012062
Author(s):  
Weihong Wang ◽  
Zhuolin Wu ◽  
Xuan Liu ◽  
Lei Jia ◽  
Xiaoguang Wang
Keyword(s):  
Image Processing ◽  
Time Series ◽  
Anomaly Detection ◽  
Performance Indicators ◽  
Clustering Algorithm ◽  
Limited Resources ◽  
Detection Model ◽  
Class Time ◽  
Operation And Maintenance ◽  
Maintenance Systems

Abstract For modern operation and maintenance systems, they are usually required to monitor multiple types and large quantities of machine’s key performance indicators (KPIs) at the same time with limited resources. In this paper, to tackle these problems, we propose a highly compatible time series anomaly detection model based on K-means clustering algorithm with a new Wavelet Feature Distance (WFD). Our work is inspired by some ideas from image processing and signal processing domain. Our model detects abnormalities in the time series datasets which are first clustered by K-means to boost the accuracy. Our experiments show significant accuracy improvements compared with traditional algorithms, and excellent compatibilities and operating efficiencies compared with algorithms based on deep learning.

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