9786202553551 - Casebook on Data Protection: Outlier Detection Using A New Hybrid Approach On Mixed Dataset de Babalola, Olumide (12 resultados)

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Taschenbuch. Condición: Neu. Outlier Detection Using A New Hybrid Approach On Mixed Dataset | Outlier Detection Using A New Hybrid Approach On Mixed Dataset | Navneet Kaur | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786202553551 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data mining is a process of extracting hidden and useful information from the data. Outlier detection is a fundamental part of data mining and has huge attention from the research community recently. An outlier is data object that deviates from other observations. Detecting outliers has important applications in data cleaning as well as in the mining of abnormal points for fraud detection, stock market analysis, intrusion detection, marketing, network sensors. Most of the existing research efforts focus on numerical datasets which are not directly applicable on categorical dataset where there is little sense in ordering the data and calculating distances among data points. Furthermore, a number of the current outlier detection methods require quadratic time with respect to the dataset size and usually need multiple scans of the data; these features are undesirable when the datasets are large. This thesis focuses and evaluates, experimentally, an outlier detection approach that is geared towards categorical sets. In addition, this is a simple, scalable and efficient outlier detection algorithm that has the advantage of discovering outliers in categorical or numerical datasets by per 64 pp. Englisch.

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kaur NavneetFirst of all, I am thankful to God for his blessings and showing me the right direction. With his mercy, it has been made possible for me to reach so far.I AM AN ASSISTANT PROFESSOR AT SHRI GURU TEG BAHADUR KHALSA COLLEGE.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data mining is a process of extracting hidden and useful information from the data. Outlier detection is a fundamental part of data mining and has huge attention from the research community recently. An outlier is data object that deviates from other observations. Detecting outliers has important applications in data cleaning as well as in the mining of abnormal points for fraud detection, stock market analysis, intrusion detection, marketing, network sensors. Most of the existing research efforts focus on numerical datasets which are not directly applicable on categorical dataset where there is little sense in ordering the data and calculating distances among data points. Furthermore, a number of the current outlier detection methods require quadratic time with respect to the dataset size and usually need multiple scans of the data; these features are undesirable when the datasets are large. This thesis focuses and evaluates, experimentally, an outlier detection approach that is geared towards categorical sets. In addition, this is a simple, scalable and efficient outlier detection algorithm that has the advantage of discovering outliers in categorical or numerical datasets by perVDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data mining is a process of extracting hidden and useful information from the data. Outlier detection is a fundamental part of data mining and has huge attention from the research community recently. An outlier is data object that deviates from other observations. Detecting outliers has important applications in data cleaning as well as in the mining of abnormal points for fraud detection, stock market analysis, intrusion detection, marketing, network sensors. Most of the existing research efforts focus on numerical datasets which are not directly applicable on categorical dataset where there is little sense in ordering the data and calculating distances among data points. Furthermore, a number of the current outlier detection methods require quadratic time with respect to the dataset size and usually need multiple scans of the data; these features are undesirable when the datasets are large. This thesis focuses and evaluates, experimentally, an outlier detection approach that is geared towards categorical sets. In addition, this is a simple, scalable and efficient outlier detection algorithm that has the advantage of discovering outliers in categorical or numerical datasets by per.