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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

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.  ...  Detecting outliers in database (as unusual objects) using Clustering and Distance-based approach is a big desire.  ...  Loureio [28] proposed a method for detecting outlier. Hierarchical clustering technique is used for detecting outliers.  ... 
doi:10.5120/1396-1885 fatcat:xcc6xfho7fbazlxm5yefinxlay

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.  ...  We propose an approximate approach for Trajectory Outlier Detection (ATODS) based on TODS by distance tree structure.  ... 
doi:10.1109/access.2020.2974521 fatcat:qoncgklt4zeqjf47kuf6msxrqu

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

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  ...  T lies greater than distance D from O, where the term DB(p, D)-outlier is a shorthand notation for a Distance-Based outlier (detected using parameters p and D)"(Knorr& Ng 1998).Given a distance measure  ... 
dblp:conf/pacis/TangHLLL16 fatcat:i3kx7pevrzgzhhg7t6qyd7mahu

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

Mining Outliers in Spatial Networks [chapter]

Wen Jin, Yuelong Jiang, Weining Qian, Anthony K. H. Tung
2006 Lecture Notes in Computer Science  
In this paper, we study the interesting problem of distance-based outliers in spatial networks.  ...  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  ...  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

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

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).  ...  LOCAL DISTANCE-BASED OUTLIER FACTOR (LDOF) LDOF [11] is a distance -based method which uses the relative location of an object to its neighbours to determine the degree to which the object deviates from  ... 
doi:10.2478/pomr-2018-0005 fatcat:morizemppzaw3ccwfwvmapcfii

Fast and Scalable Outlier Detection with Metric Access Methods [chapter]

Altamir Gomes Bispo Junior, Robson Leonardo Ferreira Cordeiro
2019 Lecture Notes in Computer Science  
the contrast for outlier detection methods that use distances as a basis.  ...  Proximity-based outlier detection methods can be further divided in distance-based methods, density-based methods and clustering-based methods.  ... 
doi:10.1007/978-3-030-22741-8_14 fatcat:j3ewkkip5va6vckbo2yz7rjzra

A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams

Omar Alghushairy, Raed Alsini, Terence Soule, Xiaogang Ma
2020 Big Data and Cognitive Computing  
This paper addresses local outlier detection. The best-known technique for local outlier detection is the Local Outlier Factor (LOF), a density-based technique.  ...  Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset.  ...  [62] proposed an algorithm, called the Relative Density Factor (RDF), which detects an outlier using p-trees. A data point pt that has a high RDF score is an outlier.  ... 
doi:10.3390/bdcc5010001 fatcat:ab3ofbim3ngpxd2jgjdbdw75qu

ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg" [article]

Erich Schubert, Arthur Zimek
2019 arXiv   pre-print
The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection.  ...  In order to achieve high performance and scalability, ELKI offers data index structures such as the R*-tree that can provide major performance gains.  ...  Simple distance based outlier detection algorithms. support(X ⇒ Y ) − support(X)support(Y ) = P (X ∩ Y ) − P (X)P (Y ) Approximate ABOD: Angle-Based Outlier Detection [KSZ08] (since 0.6.0) DBOD: Distance  ... 
arXiv:1902.03616v1 fatcat:ws3f5ymembeg3dlwawlz6njmvq

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

Detection of different outlier scenarios in circular regression model using single-linkage method

N F M Di, S Z Satari, R Zakaria
2017 Journal of Physics, Conference Series  
In this study, we proposed clustering-based method using single linkage to detect multiple outliers.  ...  Single-linkage is one of several clustering methods, where the distance between two clusters is determined by a single pair element that are closest to each other.  ...  Based on equation ( 5 ), the matrix of distance between all possible pairs of variables are calculated by using the Euclidean distance, equation (6).  ... 
doi:10.1088/1742-6596/890/1/012127 fatcat:lc7yv3xagnhxzf6aapeq3xfeem

The multiple outliers detection using agglomerative hierarchical methods in circular regression model

Siti Zanariah Satari, Nur Faraidah Muhammad Di, Roslinazairimah Zakaria
2017 Journal of Physics, Conference Series  
The single-linkage method is one of the simplest agglomerative hierarchical methods that is commonly used to detect outlier.  ...  The results show that the single-linkage method performs very well in detecting the multiple outliers with lower masking and swamping effects.  ...  A Mean Shift Based (MSBC) clustering algorithm that uses single linkage method with Euclidean distance to cluster a circular data set and the method succeeded in detecting outliers at the same time is  ... 
doi:10.1088/1742-6596/890/1/012152 fatcat:ohrsl75bnngg5jgjhrtugp6qoi
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