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Anomaly Detection for Categorical Data.
В наличии
Местонахождение: Алматы | Состояние экземпляра: новый |
Бумажная
версия
версия
Автор: Chiranji Lal Chowdhary and R. Sandhya
ISBN: 9783330326842
Год издания: 2017
Формат книги: 60×90/16 (145×215 мм)
Количество страниц: 104
Издательство: LAP LAMBERT Academic Publishing
Цена: 29611 тг
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Аннотация: 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.
Ключевые слова: Anomalies, Data Mining, Outlier detection system, holoentropy