Anomaly Detection for Categorical Data

Chiranji Lal Chowdhary

ISBN 10: 3330326840 / ISBN 13: 9783330326842

Verlag: LAP LAMBERT Academic Publishing, 2017

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - Outlier detection system discovers the novel or rare events, anomalies, vicious actions, exceptional phenomena. It is mandatory to find these anomalies in data mining because the presence of these objects usually makes the database inefficient. An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. Finding objects that do not conform to well-defined notions of expected behaviour in a dataset is called outlier detection. Outlier detection is a pre-processing step for locating these non-conforming objects in data sets. This outlier detection is a challenging process in large scale database since it has high dimensional data with low anomalous rate. Here outliers are defined formally and the optimized ways to detect outliers is also proposed here. Optimization in outlier detection is achieved by a new concept of holoentropy which combines entropy and total correlation. It is a more effective and efficient practical phenomenon in outlier detection methods. It can be used effectively to deal with both large and high-dimensional datasets.

Bestandsnummer des Verkäufers 9783330326842

Bibliografische Details

• Titel: Anomaly Detection for Categorical Data
• Verlag: LAP LAMBERT Academic Publishing
• Erscheinungsjahr: 2017
• Sprache: Englisch
• ISBN-10: 3330326840
• ISBN-13: 9783330326842
• Einband: Taschenbuch
• Zustand: Neu

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Inhaltsangabe

Outlier detection system discovers the novel or rare events, anomalies, vicious actions, exceptional phenomena. It is mandatory to find these anomalies in data mining because the presence of these objects usually makes the database inefficient. An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. Finding objects that do not conform to well-defined notions of expected behaviour in a dataset is called outlier detection. Outlier detection is a pre-processing step for locating these non-conforming objects in data sets. This outlier detection is a challenging process in large scale database since it has high dimensional data with low anomalous rate. Here outliers are defined formally and the optimized ways to detect outliers is also proposed here. Optimization in outlier detection is achieved by a new concept of holoentropy which combines entropy and total correlation. It is a more effective and efficient practical phenomenon in outlier detection methods. It can be used effectively to deal with both large and high-dimensional datasets.