A unified notion of outliers: Properties and computation (original) (raw)
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Relative Study of Outlier Detection Procedures
International Journal of Engineering Sciences and Research Technology, 2016
Data Mining just alludes to the extraction of exceptionally intriguing patterns of the data from the monstrous data sets. Outlier detection is one of the imperative parts of data mining which Rexall discovers the perceptions that are going amiss from the normal expected conduct. Outlier detection and investigation is once in a while known as Outlier mining. In this paper, we have attempted to give the expansive and a far reaching literature survey of Outliers and Outlier detection procedures under one rooftop, to clarify the lavishness and multifaceted nature connected with each Outlier detection technique. Besides, we have likewise given a wide correlation of the different strategies for the diverse Outlier techniques. Outliers are the focuses which are unique in relation to or conflicting with whatever is left of the information. They can be novel, new, irregular, strange or uproarious data. Outliers are in some cases more fascinating than most of the information. The principle di...
arXiv: Probability, 2017
We define outliers as a set of observations which contradicts the proposed mathematical (statistical) model and we discuss the frequently observed types of the outliers. Further we explore what changes in the model have to be made in order to avoid the occurance of the outliers. We observe that some variants of the outliers lead to classical results in probability, such as the law of large numbers and the concept of heavy tailed distributions. Key words: outlier; the law of large numbers; heavy tailed distributions; model rejection.
Outlier Detection: Applications And Techniques
2012
Outliers once upon a time regarded as noisy data in statistics, has turned out to be an important problem which is being researched in diverse fields of research and application domains. Many outlier detection techniques have been developed specific to certain application domains, while some techniques are more generic. Some application domains are being researched in strict confidentiality such as research on crime and terrorist activities. The techniques and results of such techniques are not readily forthcoming. A number of surveys, research and review articles and books cover outlier detection techniques in machine learning and statistical domains individually in great details. In this paper we make an attempt to bring together various outlier detection techniques, in a structured and generic description. With this exercise, we hope to attain a better understanding of the different directions of research on outlier analysis for ourselves as well as for beginners in this research field who could then pick up the links to different areas of applications in details.
IEEE Paper - METHODS TO DETECT DIFFERENT TYPES OF OUTLIERS
Outliers are those data that deviates significantly from the remaining data. Outliers has emerging applications in irregular credit card transactions, used to find credit card fraud, or identifying patients who shows abnormal symptoms due to suffering from a particular type of disease. This paper gives an idea about the various approaches and techniques used in outlier detection and the areas in which outlier detection is used and also about how outlier detection is handled in higher dimensional data.
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Learning Objectives • What common methods for outlier detection are available. • How to choose the most appropriate methods. • How to assess the performance of an outlier detection method and how to compare different methods.
Finding intensional knowledge of distance-based outliers
Proceedings of the 25th International Conference on Very …, 1999
Existing studies on outliers focus only on the identi cation aspect; none provides any intensional knowledge of the outliers|by which we mean a description or an explanation of why an identi ed outlier is exceptional. For many applications, a description or explanation is at least as vital to the user as the identication aspect. Speci cally, intensional knowledge helps the user to: (i) evaluate the validity of the identi ed outliers, and (ii) improve one's understanding of the data.
Local Outlier Detection with Interpretation
Advanced Information Systems Engineering, 2013
Outlier detection aims at searching for a small set of objects that are inconsistent or considerably deviating from other objects in a dataset. Existing research focuses on outlier identification while omitting the equally important problem of outlier interpretation. This paper presents a novel method named LODI to address both problems at the same time. In LODI, we develop an approach that explores the quadratic entropy to adaptively select a set of neighboring instances, and a learning method to seek an optimal subspace in which an outlier is maximally separated from its neighbors. We show that this learning task can be solved via the matrix eigen-decomposition and its solution contains essential information to reveal features that are most important to interpret the exceptional properties of outliers. We demonstrate the appealing performance of LODI via a number of synthetic and real world datasets and compare its outlier detection rates against state-of-the-art algorithms.
OPTICS-OF: Identifying Local Outliers
Lecture Notes in Computer Science, 1999
For many KDD applications finding the outliers, i.e. the rare events, is more interesting and useful than finding the common cases, e.g. detecting criminal activities in E-commerce. Being an outlier, however, is not just a binary property. Instead, it is a property that applies to a certain degree to each object in a data set, depending on how 'isolated' this object is, with respect to the surrounding clustering structure. In this paper, we formally introduce a new notion of outliers which bases outlier detection on the same theoretical foundation as density-based cluster analysis. Our notion of an outlier is 'local' in the sense that the outlier-degree of an object is determined by taking into account the clustering structure in a bounded neighborhood of the object. We demonstrate that this notion of an outlier is more appropriate for detecting different types of outliers than previous approaches, and we also present an algorithm for finding them. Furthermore, we show that by combining the outlier detection with a density-based method to analyze the clustering structure, we can get the outliers almost for free if we already want to perform a cluster analysis on a data set.
2016
Data Mining just alludes to the extraction of exceptionally intriguing patterns of the data from the monstrous data sets. Outlier detection is one of the imperative parts of data mining which Rexall discovers the perceptions that are going amiss from the normal expected conduct. Outlier detection and investigation is once in a while known as Outlier mining. In this paper, we have attempted to give the expansive and a far reaching literature survey of Outliers and Outlier detection procedures under one rooftop, to clarify the lavishness and multifaceted nature connected with each Outlier detection technique. Besides, we have likewise given a wide correlation of the different strategies for the diverse Outlier techniques. Outliers are the focuses which are unique in relation to or conflicting with whatever is left of the information. They can be novel, new, irregular, strange or uproarious data. Outliers are in some cases more fascinating than most of the information. The principle di...
Outlier Detection based on Distance: Reverse Nearest Neighbors
Outlier identification previously, high-dimensional information displays Different tests coming about because of the " curse of dimensionality ". An prevailing perspective may be that separation concentration, i.e., the propensity from claiming distances over high-dimensional information on turned into indiscernible, Hinders those identification about outliers by making distance-based strategies name at focuses as very nearly just as beneficial outliers. In this paper, we gatherings give proof supporting the assumption that such a see will be a really simple, toward demonstrating that distance-based strategies could. Handle All the more differentiating outlier scores previously, high-dimensional settings. Furthermore, we demonstrate that helter skelter dimensionality could have. An alternate impact, toward reexamining those ideas about opposite closest neighbours in the unsupervised outlier-detection connection. Namely, it might have been as of late watched that those dissemination about points' reverse-neighbour tallies turns into skewed on secondary dimensions, bringing about. The wonder known as hubness. We furnish knowledge under how a few focuses (antihubs) show up extremely rarely to k-NN records about. Different points, What's more clarify those association between antihubs, outliers, and existing unsupervised outlier-detection systems .Toward. Assessing that excellent k-NN method that angle-based method outlined to high-dimensional data, those density-based nearby. Outlier component What's more impacted outlierness methods, also antihubs-based strategies on Different manufactured Also real-world information sets, we offer novel knowledge under the convenience about opposite neighbour tallies previously, unsupervised outlier identification.