Outlier Detection for Different Applications: Review

International Journal of Computer Applications, 2013

Outlier detection is an extremely important problem with direct application in a wide variety of domains. A key challenge with outlier detection is that it is not a wellformulated problem like clustering. In this paper, discussion on different techniques and then comparison by analyzing their different aspects, essentially, time complexity. Every unique problem formulation entails a different approach, resulting in a huge literature on outlier detection techniques. Several techniques have been proposed to target a particular application domain. The classification of outlier detection techniques based on the applied knowledge discipline provides an idea of the research done by different communities and also highlights the unexplored research avenues for the outlier detection problem. Discussed of the behavior of different techniques will be done, in this paper, with respect to the nature. The feasibility of a technique in a particular problem setting also depends on other constraints. For example, Statistical techniques assume knowledge about the underlying distribution characteristics of the data. Distance based techniques are typically expensive and hence are not applied in scenarios where computational complexity is an important issue.

— Outlier is defined as an event that deviates too much from other events. The identification of outlier can lead to the discovery of useful and meaningful knowledge. Outlier means it's happen at some time it's not regular activity. Research about Detection of Outlier has been extensively studies in the past decade. However, most existing research focused on the algorithm based on specific knowledge, compared with outlier detection approach is still rare. In this paper mainly focused on different kind of outlier detection approaches and compares it's prone and cones. In this paper we mainly distribute of outlier detection approach in two parts classic outlier approach and spatial outlier approach. The classical outlier approach identifies outlier in real transaction dataset, which can be grouped into statistical approach, distance approach, deviation approach, and density approach. The spatial outlier approach detect outlier based on spatial dataset are different from transaction data, which can be categorized into spaced approach and graph approach. Finally, the comparison of outlier detection approaches.

International Journal of Computer Applications, 2013

Data Mining is used to extract useful information from a collection of databases or data warehouses. In recent years, Data Mining has become an important field. This paper has surveyed upon data mining and its various techniques that are used to extract useful information such as clustering, and has also surveyed the techniques that are used to detect the outliers. This paper also presents various techniques used by different researchers to detect outliers and present the efficient result to the user.

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...

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 Access

Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and organized review of the progress of outlier detection methods from 2000-2019. Firstly, we offer the fundamental concepts of outlier detection and then categorize them into different techniques from diverse outlier detection techniques such as distance-, clustering-, density-, ensemble-, and learning-based methods. In each category, we introduce some state-ofthe-art outlier detection methods and further discuss them in detail in terms of their performance. Secondly, we delineate their pros, cons, and challenges to provide researchers with a concise overview of each technique and recommend solutions and possible research directions. This paper gives current progress of outlier detection techniques and provides a better understanding of the different outlier detection methods. The open research issues and challenges at the end will provide researchers with a clear path for the future of outlier detection methods.

2015

Outlier detection is the process of finding the data that behave very differently from the normal expected behavior. Outliers may be due to system errors, noise or human intended action. Outlier detection becomes an important task in any application to provide reliable and effective results. Many outlier detection methods are proposed amongst SVDD is most commonly used method. This paper provides a brief survey on the outlier detection methods.

Artificial Intelligence Review, 2004

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review.

2015

In data mining outlier detection refers to the recognition of data point which does not follow the expected pattern or behavior in a particular dataset or is significantly different from other points in a data. In this paper we will review some of the outlier detection techniques and discuss their advantages and disadvantages with respect to various aspects. Outlier detection techniques can be classified into three modes namely unsupervised, semi-supervised and supervised. But, unsupervised outlier detection methods can be further classified as distance based or density based. Many outlier detection techniques are proposed till date. These proposed techniques can be broadly categorized as distribution based (statistical), clustering-based, density-based and model-based

2016

Data Mining simply refers to the mining of very interesting patterns of the data from the massive data sets. Outlier detection is one of the important characteristics of data mining. It is a task that finds objects that are considerably dissimilar, incomparable or inconsistent with respect to the remaining data. Outlier detection has wide applications which include data analysis, network intrusion detection, financial fraud detection, and clinical diagnosis of diseases. This paper proposes three outlier detection models such as OFWDT (Outlier Finding with Decision Tree), OFWNB (Outlier Finding with Naïve Bayes) and OFWQR (Outlier Finding With Quartile Range) with three different applications. OFWDT model has three steps of a process. In the first step, groups the data in to number of clusters using Farthest First clustering algorithm. Due to minimize the size of dataset, the computation time reduced greatly.In the second step, outliers are detected from wisconsin breast cancer datas...