n-INCLOF: A dynamic local outlier detection algorithm for data streams

2010 ◽  
Author(s):  
Ke Gao ◽  
Feng-Jing Shao ◽  
Ren-Cheng Sun
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Detection Algorithm ◽  
Local Outlier

Scalable KDE-based top-n local outlier detection over large-scale data streams

2020 ◽  
Vol 204 ◽  
pp. 106186 ◽  
Author(s):  
Fang Liu ◽  
Yanwei Yu ◽  
Peng Song ◽  
Yangyang Fan ◽  
Xiangrong Tong
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data ◽  
Local Outlier

Local Outlier Detection Algorithm Based on Coefficient of Variation

2014 ◽  
Vol 635-637 ◽  
pp. 1723-1728
Author(s):  
Shi Bo Zhou ◽  
Wei Xiang Xu
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Coefficient Of Variation ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Data Set ◽  
Outliers Detection ◽  
Deviation Factor ◽  
Local Deviation ◽  
Local Outlier

Local outliers detection is an important issue in data mining. By analyzing the limitations of the existing outlier detection algorthms, a local outlier detection algorthm based on coefficient of variation is introduced. This algorthms applies K-means which is strong in outliers searching, divides data set into sections, puts outliers and their nearing clusters into a local neighbourhood, then figures out the local deviation factor of each local neighbourhood by coefficient of variation, as a result, local outliers can more likely be found.The heoretic analysis and experimental results indicate that the method is ef fective and efficient.

scholarly journals TADILOF: Time Aware Density-Based Incremental Local Outlier Detection in Data Streams

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5829 ◽  
Author(s):  
Jen-Wei Huang ◽  
Meng-Xun Zhong ◽  
Bijay Prasad Jaysawal
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Data Stream ◽  
State Of The Art ◽  
Streaming Data ◽  
Current State ◽  
Data Points ◽  
Local Outlier ◽  
Time Aware ◽  
Over Time

Outlier detection in data streams is crucial to successful data mining. However, this task is made increasingly difficult by the enormous growth in the quantity of data generated by the expansion of Internet of Things (IoT). Recent advances in outlier detection based on the density-based local outlier factor (LOF) algorithms do not consider variations in data that change over time. For example, there may appear a new cluster of data points over time in the data stream. Therefore, we present a novel algorithm for streaming data, referred to as time-aware density-based incremental local outlier detection (TADILOF) to overcome this issue. In addition, we have developed a means for estimating the LOF score, termed "approximate LOF," based on historical information following the removal of outdated data. The results of experiments demonstrate that TADILOF outperforms current state-of-the-art methods in terms of AUC while achieving similar performance in terms of execution time. Moreover, we present an application of the proposed scheme to the development of an air-quality monitoring system.

Fast Memory Efficient Local Outlier Detection in Data Streams (Extended Abstract)

2017 ◽  
Author(s):  
Mahsa Salehi ◽  
Christopher Leckie ◽  
James C. Bezdek ◽  
Tharshan Vaithianathan ◽  
Xuyun Zhang
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Fast Memory ◽  
Local Outlier ◽  
Memory Efficient

scholarly journals An Incremental Local Outlier Detection Method in the Data Stream

2018 ◽  
Vol 8 (8) ◽  
pp. 1248 ◽  
Author(s):  
Haiqing Yao ◽  
Xiuwen Fu ◽  
Yongsheng Yang ◽  
Octavian Postolache
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Data Stream ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Detection Accuracy ◽  
K Nearest Neighbor ◽  
Major Work ◽  
Wide Range ◽  
Local Outlier

Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging. Recent major work of outlier detection has focused on principle research of the local outlier factor, and there are few studies on incremental updating strategies, which are vital to outlier detection in data streams. In this paper, a novel incremental local outlier detection approach is introduced to dynamically evaluate the local outlier in the data stream. An extended local neighborhood consisting of k nearest neighbors, reverse nearest neighbors and shared nearest neighbors is estimated for each data. The theoretical evidence of algorithm complexity for the insertion of new data and deletion of old data in the composite neighborhood shows that the amount of affected data in the incremental calculation is finite. Finally, experiments performed on both synthetic and real datasets verify its scalability and outlier detection accuracy. All results show that the proposed approach has comparable performance with state-of-the-art k nearest neighbor-based methods.

Fast local outlier detection algorithm using K kernel space

2019 ◽  
Vol 19 (3) ◽  
pp. 751-764
Author(s):  
Zhong-Ping Zhang ◽  
Ming-Yue Shi ◽  
Cong Liu ◽  
Jing-Yang Qiu ◽  
Jie Qi
Keyword(s):  
Outlier Detection ◽  
Detection Algorithm ◽  
Kernel Space ◽  
Local Outlier
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