[BOOK][B] An introduction to outlier analysis
CC Aggarwal, CC Aggarwal - 2017 - Springer
CC Aggarwal, CC Aggarwal
2017•SpringerOutliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more
generating processes, which could either reflect activity in the system or observations
collected about entities. When the generating process behaves unusually, it results in the
creation of outliers. Therefore, an outlier often contains useful information about abnormal
characteristics of the systems and entities that impact the data generation process. The …
mining and statistics literature. In most applications, the data is created by one or more
generating processes, which could either reflect activity in the system or observations
collected about entities. When the generating process behaves unusually, it results in the
creation of outliers. Therefore, an outlier often contains useful information about abnormal
characteristics of the systems and entities that impact the data generation process. The …
Abstract
Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data mining and statistics literature. In most applications, the data is created by one or more generating processes, which could either reflect activity in the system or observations collected about entities. When the generating process behaves unusually, it results in the creation of outliers. Therefore, an outlier often contains useful information about abnormal characteristics of the systems and entities that impact the data generation process. The recognition of such unusual characteristics provides useful application-specific insights.
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