Outlier Analysis

Target entity: Outlier Analysis
Context triple: [Charu C. Aggarwal, notableWork, Outlier Analysis]

• A. Anomaly Detector
  Anomaly Detector is an Azure Cognitive Services offering that uses machine learning to automatically detect unusual patterns and outliers in time-series or other data.
• B. Exploratory Data Analysis
  Exploratory Data Analysis is a statistical approach, popularized by John W. Tukey, that focuses on using visual and quantitative techniques to summarize data, uncover patterns, and suggest hypotheses before formal modeling.
• C. Mining of Massive Datasets
  "Mining of Massive Datasets" is a widely used textbook that introduces practical and scalable data mining and machine learning techniques for analyzing large-scale datasets.
• D. Mahalanobis distance
  Mahalanobis distance is a multivariate measure of the distance between a point and a distribution (or between distributions) that accounts for correlations between variables via the covariance matrix.
• E. Tukey's fences
  Tukey's fences are a statistical rule-of-thumb method for identifying outliers in a data set using interquartile range–based cutoff points.
• F. None of above. chosen
• G. Unsure - the case is ambiguous/there is not enough information to decide.

Target entity: Outlier Analysis
Target entity description: Outlier Analysis is a comprehensive book by Charu C. Aggarwal that systematically covers the theory, algorithms, and applications of detecting anomalous data in various domains.

• A. Anomaly Detector
  Anomaly Detector is an Azure Cognitive Services offering that uses machine learning to automatically detect unusual patterns and outliers in time-series or other data.
• B. Exploratory Data Analysis
  Exploratory Data Analysis is a statistical approach, popularized by John W. Tukey, that focuses on using visual and quantitative techniques to summarize data, uncover patterns, and suggest hypotheses before formal modeling.
• C. Mining of Massive Datasets
  "Mining of Massive Datasets" is a widely used textbook that introduces practical and scalable data mining and machine learning techniques for analyzing large-scale datasets.
• D. Mahalanobis distance
  Mahalanobis distance is a multivariate measure of the distance between a point and a distribution (or between distributions) that accounts for correlations between variables via the covariance matrix.
• E. Tukey's fences
  Tukey's fences are a statistical rule-of-thumb method for identifying outliers in a data set using interquartile range–based cutoff points.
• F. None of above. chosen