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Outlier Detection for Temporal Data

have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large;

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Outlier Detection Techniques and Applications

of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data;

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New Developments in Unsupervised Outlier Detection

perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for;

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Outlier Analysis

, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains;

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Outlier Analysis

, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains;

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Advances in Knowledge Discovery and Data Mining

learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; outlier and anomaly detection; mining;

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Advances in Knowledge Discovery and Data Mining

learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; outlier and anomaly detection; mining;

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Outlier Ensembles

) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the;

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Database Systems for Advanced Applications

index; spatio-temporal data; modern computing platform; social networks; information integration and data quality; information retrieval and;

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Let's Get Rich to Save Energy

, but not for GDP per capita in all specifications. Data was subject to outlier detection and adjustments for fixed effects for both cross;

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Robust Representation for Data Analytics

data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection;

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Application of Geostationary Satellites in Forest Fire Detection

book, therefore, provides a new multi-temporal threshold algorithm for near real-time forest fire detection using geostationary satellites;

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Anomaly Detection

of data is called anomaly detection. With progress in the technologies and the widespread use of data for the purpose for business the increase;

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Data Fitting and Uncertainty

source code in ANSI-C for fitting with weighted least squares including outlier detection. Among others the book covers following topics;

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Finding Ghosts in Your Data

language to build a robust service for anomaly detection against a variety of data types. The book starts with an overview of what anomalies and;

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R and Data Mining

, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more. Data;

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Developing VO2max Prediction Models Using Machine Learning Methods

performance of the proposed approach has been compared with the two widely used outlier detection algorithms. The results show that the improved SVM;

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Anomaly Detection and Complex Event Processing Over IoT Data Streams

advanced processing techniques for IoT data streams and the anomaly detection algorithms over them. The book brings new advances and generalized;

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Intro To Statistical Analysi Of Lab Data

comparison procedures, limit and bias detection, outlier analysis and detecting sources of variation * Analysis of robustness and ruggedness;

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Finding Ghosts in Your Data

language to build a robust service for anomaly detection against a variety of data types. The book starts with an overview of what anomalies and;

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Advances in Knowledge Discovery and Data Mining

-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system;

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Advances in Knowledge Discovery and Data Mining

-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system;

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Data Mining and Data Visualization

estimation, tree-based methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality reduction. The third section focuses;

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Statistical Learning for Big Dependent Data

ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New;

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Chemometrics with R

specific topics in the area of chemometrics, such as outlier detection, and biomarker identification. The corresponding R code is provided for all;

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Reverse Clustering

detection, feature selection, and even factor analysis as well as geometry of the data set. The book is useful for all those who look for new;

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Clustering And Outlier Detection For Trajectory Stream Data

services.Unlike other books on spatial databases, mobile computing, data mining, or computing with spatial trajectories, this book is focused on smart;

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