anomaly detection categorical data von chowdhary chiranji (5 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Englisch.

Paperback. Zustand: Brand New. 104 pages. 8.66x5.91x0.24 inches. In Stock.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chowdhary Chiranji LalChiranji Lal Chowdhary working as Assistant Professor (Selection Grade) at VIT Vellore in the School of Information Technology and Engineering. Prior to coming to VIT, he was a Lecturer at the M.S. Ramaiah Insti.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 104 pp. Englisch.

Taschenbuch. Zustand: Neu. 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.