1# Library to Find Best eBook for Read
Author : Wobbe Pieter Zijlstra
Release : 2009
Genre :
Kind : eBook
Book Rating : 328/5 ( reviews)
Book Synopsis Outlier Detection in Test and Questionnaire Data for Attribute Measurement by : Wobbe Pieter Zijlstra
Download or read book Outlier Detection in Test and Questionnaire Data for Attribute Measurement written by Wobbe Pieter Zijlstra. This book was released on 2009. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Charu C. Aggarwal
Release : 2013-01-11
Genre : Computers
Kind : eBook
Book Rating : 963/5 ( reviews)
Book Synopsis Outlier Analysis by : Charu C. Aggarwal
Download or read book Outlier Analysis written by Charu C. Aggarwal. This book was released on 2013-01-11. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions– the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered.
Author : N. N. R. Ranga Suri
Release : 2019-01-10
Genre : Technology & Engineering
Kind : eBook
Book Rating : 277/5 ( reviews)
Book Synopsis Outlier Detection: Techniques and Applications by : N. N. R. Ranga Suri
Download or read book Outlier Detection: Techniques and Applications written by N. N. R. Ranga Suri. This book was released on 2019-01-10. Available in PDF, EPUB and Kindle. Book excerpt: This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges.
Author : Manish Gupta
Release : 2022-06-01
Genre : Computers
Kind : eBook
Book Rating : 059/5 ( reviews)
Book Synopsis Outlier Detection for Temporal Data by : Manish Gupta
Download or read book Outlier Detection for Temporal Data written by Manish Gupta. This book was released on 2022-06-01. Available in PDF, EPUB and Kindle. Book excerpt: Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies
Author : Ke Zhang
Release : 2016
Genre :
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Towards Outlier Detection for Scattered Data and Mixed Attribute Data by : Ke Zhang
Download or read book Towards Outlier Detection for Scattered Data and Mixed Attribute Data written by Ke Zhang. This book was released on 2016. Available in PDF, EPUB and Kindle. Book excerpt: Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world knowledge discovery and data mining (KDD) applications. The research work in this thesis starts with a critical review on the latest and most popular methodologies available in outlier detection area. Based on a series of performance evaluation of these algorithms, two major issues that exist in outlier detection, namely scattered data problem and mixed attribute problem, are identified, and then being further addressed by the novel approaches proposed in this thesis. Based on our review and evaluation it has been found that the existing outlier detection methods are ineffective for many real-world scatter datasets, due to the implicit data patterns within these sparse datasets. In order to address this issue, we define a novel Local Distance-based Outlier Factor (LDOF) to measure the outlierness of objects in scattered datasets. LDOF uses the relative location of an object to its neighbours to determine the degree that the object deviates from its neighbourhood. The characteristics of LDOF are theoretically analysed, including LDOF's lower bound, false-detection probabilities, as well as its parameter range tolerance. In order to facilitate parameter settings in real-world applications, we employ a top-n technique in the proposed outlier detection approach, where only the objects with the highest LDOF values are regarded as outliers. Compared to conventional approaches (such as top-n KNN and top-n LOF), our method, top-n LDOF, proved more effective for detecting outliers in scattered data. The parameter settings for LDOF is also more practical for real-world applications, since its performance is relatively stable over a large range of parameter values, as illustrated by experimental results on both real-world and synthetic datasets. Secondly, for the mixed attribute problem, traditional outlier detection methods often fail to effectively identify outliers, due to the lack of the mechanisms to consider the interactions among various types of the attributes that might exist in the real-world datasets. To address this issue in mixed attribute datasets, we propose a novel Pattern based Outlier Detection approach (POD). A pattern in this thesis is defined as a mathematical representation that describes the majority of the observations in datasets and captures the interactions among different types of attributes. The POD is designed in the way that the more an object deviates from these patterns, the higher its outlier factor is. We simply use logistic regression to learn patterns and then formulate the outlier factor in mixed attribute datasets. For the datasets which outliers are randomly allocated among normal data objects, distance based methods, i.e. LOF and KNN, would not have effective. On the contrary, as the outlierness definition proposed in POD is able to integrate numeric and categorical attributes into a united definition, the numeric attributes would not represent the final outlierness directly but contribute their anomaly through categorical attributes. Therefore, the POD will be able to offer considerably performance improvement compared to those traditional methods. A series of experiments show that the performance enhancement by the POD is statistically significant comparing to several classic outlier detection methods. However, for POD, the algorithm sometimes shows lower detection precision for some mixed attribute datasets, because POD has a strong assumption that the observed mixed attribute dataset in any subspace is linearly separable. This limitation is determined by the linear classifier, logistic regression, we used in POD algorithm.
