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A Fast Randomized Method for Local Density-Based Outlier Detection in High Dimensional Data [chapter]

Minh Quoc Nguyen, Edward Omiecinski, Leo Mark, Danesh Irani
2010 Lecture Notes in Computer Science  
Local density-based outlier (LOF) is a useful method to detect outliers because of its model free and locally based property. However, the method is very slow for high dimensional datasets.  ...  Based on a consistency property of outliers, random points are selected to partition a data set to compute outlier candidates locally.  ...  Bay and Schwabacher [2] introduce a randomize method to detect distance-based outlier. However, their method can not be used for density-based outlier.  ... 
doi:10.1007/978-3-642-15105-7_17 fatcat:zakor2bxgzgytginlzkgp7lgiq

Trajectory Outlier Detection on Trajectory Data Streams

Keyan Cao, Yefan Liu, Gongjie Meng, Haoli Liu, Anchen Miao, Jingke Xu
2020 IEEE Access  
In this paper, we propose a lightweight method to measure the outlier in trajectory data streams.  ...  Furthermore, we propose a basic algorithm (Trajectory Outlier Detection on trajectory data Streams-TODS), which can quickly determine the nature of the trajectory.  ...  [11] proposed an outlier trajectory detection algorithm based on R-Tree.  ... 
doi:10.1109/access.2020.2974521 fatcat:qoncgklt4zeqjf47kuf6msxrqu

Randomized outlier detection with trees

Sebastian Buschjäger, Philipp-Jan Honysz, Katharina Morik
2020 International Journal of Data Science and Analytics  
We show that GIF outperforms three competing tree-based methods and has a competitive performance to other nearest-neighbor approaches while having a lower runtime.  ...  AbstractIsolation forest (IF) is a popular outlier detection algorithm that isolates outlier observations from regular observations by building multiple random isolation trees.  ...  To view a copy of this licence, visit http://creativecomm ons.org/licenses/by/4.0/.  ... 
doi:10.1007/s41060-020-00238-w fatcat:6o327pzfvfh6rjgabvyhwintam

RODA: A fast outlier detection algorithm supporting multi-queries

Xite Wang, Jiafan Li, Mei Bai, Qian Ma
2021 IEEE Access  
For single query processing, we first extended the R-tree index and proposed a new outlier estimation method.  ...  To solve this problem, in this paper, an efficient algorithm, R-tree based Outlier Detection Algorithm (RODA), is proposed, which can effectively support single query and multiple query processing.  ...  A. BASIC PROCESSING FRAMEWORK BASED ON R-TREE This section briefly introduces the R-tree based outlier detection method, which is a popular processing framework used in many literatures.  ... 
doi:10.1109/access.2021.3058660 fatcat:z3f3iwqihfaqhj2s7vefrbymj4

Discovering Local Outliers using Dynamic Minimum Spanning Tree with Self-Detection of Best Number of Clusters

S. John Peter
2010 International Journal of Computer Applications  
In this paper we propose a new algorithm to detect outliers based on minimum spanning tree clustering and distance-based approach.  ...  Small clusters are then determined and considered as outliers. The rest of the outliers (if any) are then detected in the clusters using Distance-based method.  ...  Loureio [28] proposed a method for detecting outlier. Hierarchical clustering technique is used for detecting outliers.  ... 
doi:10.5120/1396-1885 fatcat:xcc6xfho7fbazlxm5yefinxlay

Outlier Detection via Minimum Spanning Tree

Xin Tang, Wei Huang, Xue Li, Shengli Li, Yuewen Liu
2016 Pacific Asia Conference on Information Systems  
In this paper, we combine the distance-based and clustering-based outlier detection methods, use the theory of minimum spanning tree and standard normal distribution to define a new method of outlier detection  ...  During the first phase, we build a minimum spanning tree by all data records, compute the average weight and the standard deviation of it. In the second phase, we use the distance of each data  ...  In this paper, we propose a simple but effective outlier detection method which is based on the normal distribution of the edges in the minimum spanning tree (MST) of a given data set.  ... 
dblp:conf/pacis/TangHLLL16 fatcat:i3kx7pevrzgzhhg7t6qyd7mahu

Comparative Study of Clustering-Based Outliers Detection Methods in Circular-Circular Regression Model

Siti Zanariah Satari, Nur Faraidah Muhammad Di, Yong Zulina Zubairi, Abdul Ghapor Hussin
2021 Sains Malaysiana  
Three measures of similarity based on the circular distance were used to obtain a cluster tree using the agglomerative hierarchical methods.  ...  This paper is a comparative study of several algorithms for detecting multiple outliers in circular-circular regression model based on the clustering algorithms.  ...  However, the result has showed that this method is more sensitive as it can falsely detect clean observation as outliers. in this study, a comparative study of clustering-based outliers detection methods  ... 
doi:10.17576/jsm-2021-5006-24 fatcat:frpblkrjtbdrtlwlop4l7tlbxe

Diagnosis for Early Stage of Breast Cancer using Outlier Detection Algorithm Combined with Classification Technique

2019 International Journal of Engineering and Advanced Technology  
The second stage, the outlier detection (OD) algorithm has used to detect the outliers from the cancer dataset.  ...  The proposed method has a process of three stages. First, data objects are grouped into clusters using k-means clustering algorithm.  ...  Then apply outlier Detection algorithm for detect the outliers. Finally the decision tree classifier used to classifying the data into either benign or malignant. A.  ... 
doi:10.35940/ijeat.b4514.129219 fatcat:qrumy7lsorhllcnn65lkxemvja

Detection of Multicamera Pedestrian Trajectory Outliers in Geographic Scene

Wei Wang, Yujia Xie, Xiaozhi Wang, Mohammad Farukh Hashmi
2022 Wireless Communications and Mobile Computing  
Related experiments show that our method can effectively improve the efficiency and accuracy of detecting trajectory outliers, which can enhance the early warning capability of video surveillance systems  ...  However, the current abnormal detection methods cannot effectively perceive the cross-camera abnormal movements of video objects.  ...  Fund of State Key Laboratory of Surveying, Mapping and Remote Sensing Information Engineering, Wuhan University, under (Grant no. 21S03), and the Research on Video-Geographic Scene Fusion Expression Based  ... 
doi:10.1155/2022/6516577 fatcat:uegm3q7q3bcjvkqdxzbxzidmhi

A Survey of Outlier Detection Methods in Network Anomaly Identification

P. Gogoi, D. K. Bhattacharyya, B. Borah, J. K. Kalita
2011 Computer journal  
In this paper, we present a comprehensive survey of well known distance-based, density-based and other techniques for outlier detection and compare them.  ...  We provide definitions of outliers and discuss their detection based on supervised and unsupervised learning in the context of network anomaly detection.  ...  Decision tree learners use a method known as divide and conquer to construct a suitable tree from a training set.  ... 
doi:10.1093/comjnl/bxr026 fatcat:smdimqftezaufcdgcebjz5exmq

Outlier Detection in Ocean Wave Measurements by Using Unsupervised Data Mining Methods

Kumars Mahmoodi, Hassan Ghassemi
2018 Polish Maritime Research  
In this study, three typical outlier detection algorithms:Box-plot (BP), Local Distance-based Outlier Factor (LDOF), and Local Outlier Factor (LOF) methods are used to detect outliers in significant wave  ...  Then, Hs prediction has been modelled with and without the presence of outliers by using Regression trees (RTs).  ...  In this research the voting method is used to better detect the outliers . Voting is not a new method and uses the results of other methods to detect outliers.  ... 
doi:10.2478/pomr-2018-0005 fatcat:morizemppzaw3ccwfwvmapcfii

Robust automatic methods for outlier and error detection

Ray Chambers, Adão Hentges, Xinqiang Zhao
2004 Journal of the Royal Statistical Society: Series A (Statistics in Society)  
The second uses a robust regression tree modelling procedure to identify errors. Both approaches can be implemented on a univariate basis or on a multivariate basis.  ...  The second uses a robust regression tree modelling procedure to identify errors. Both approaches can be implemented on a univariate basis or on a multivariate basis.  ...  Here we just note that both the forward search and robust regression tree methods for outlier and error detection imply methods for correcting these detected values.  ... 
doi:10.1111/j.1467-985x.2004.00748.x fatcat:oeto34ekpbckdnpjthwbk42aky

Anomaly detection by combining decision trees and parametric densities

Matthias Reif, Markus Goldstein, Armin Stahl, Thomas M. Breuel
2008 Pattern Recognition (ICPR), Proceedings of the International Conference on  
The proposed method combines the advantages of classification trees with the benefit of a more accurate representation of the outliers.  ...  In this paper a modified decision tree algorithm for anomaly detection is presented.  ...  Acknowledgment This work is part of NetCentric Security, a project of Deutsche Telekom Laboratories supported by German Research Center for Artificial Intelligence DFKI GmbH.  ... 
doi:10.1109/icpr.2008.4761796 dblp:conf/icpr/ReifGSB08 fatcat:33ryubejrzf5llky4qkjait4wy

Mining Outliers in Spatial Networks [chapter]

Wen Jin, Yuelong Jiang, Weining Qian, Anthony K. H. Tung
2006 Lecture Notes in Computer Science  
We propose an efficient mining method which partitions each edge of a spatial network into a set of length d segments, then quickly identifies the outliers in the remaining edges after pruning those unnecessary  ...  In this paper, we study the interesting problem of distance-based outliers in spatial networks.  ...  For instance, if we apply the well-known cell-based outlier detection method [12] to this graph, P 12 , P 19 and P 26 will be grouped into a small cell, and will be treated as non-outliers.  ... 
doi:10.1007/11733836_13 fatcat:cnp6vysfbbg5lcpi3z5xvyrwbe

Continuous Adaptive Outlier Detection on Distributed Data Streams [chapter]

Liang Su, Weihong Han, Shuqiang Yang, Peng Zou, Yan Jia
2007 Lecture Notes in Computer Science  
In this paper, we focus on the outlier detection over distributed data streams in real time, firstly, we formalize the problem of outlier detection using the kernel density estimation technique.  ...  In many applications, stream data are too voluminous to be collected in a central fashion and often transmitted on a distributed network.  ...  A point p in a data set T is a distance-based outlier (DB(ρ, r)-outlier) if at most a fraction ρ of the points in T lie within distance r from p.  ... 
doi:10.1007/978-3-540-75444-2_13 fatcat:5isc4rjeo5cophtmzeq3yqk4qy
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