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Understanding High Dimensional Spaces by Skillicorn David (17 results)
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Understanding High-Dimensional Spaces (SpringerBriefs in Computer Science)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Understanding High-Dimensional Spaces
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Understanding High-Dimensional Spaces
Published by Springer Berlin Heidelberg, 2012
ISBN 10: 3642333974 ISBN 13: 9783642333972
Language: English
£ 47.46
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values. Such spaces are not easy to work with because of their high dimensionality: our intuition about space is not reliable, and measures such as distance do not provide as clear information as we might expect. There are three main areas where complex high dimensionality and large datasets arise naturally: data collected by online retailers, preference sites, and social media sites, and customer relationship databases, where there are large but sparse records available for each individual; data derived from text and speech, where the attributes are words and so the corresponding datasets are wide, and sparse; and data collected for security, defense, law enforcement, and intelligence purposes, where the datasets arelarge and wide. Such datasets are usually understood either by finding the set of clusters they contain or by looking for the outliers, but these strategies conceal subtleties that are often ignored. In this book the author suggests new ways of thinking about high-dimensional spaces using two models: a skeleton that relates the clusters to one another; and boundaries in the empty space between clusters that provide new perspectives on outliers and on outlying regions. The book will be of value to practitioners, graduate students and researchers.
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Understanding High-Dimensional Spaces
Published by Springer-Verlag New York Inc, 2013
ISBN 10: 3642333974 ISBN 13: 9783642333972
Language: English
Paperback. Condition: Brand New. 2012 edition. 117 pages. 9.21x0.71x6.18 inches. In Stock.
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Understanding High-Dimensional Spaces
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Sep 2012, 2012
ISBN 10: 3642333974 ISBN 13: 9783642333972
Language: English
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Add to basketTaschenbuch. Condition: Neu. Neuware -High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values. Such spaces are not easy to work with because of their high dimensionality: our intuition about space is not reliable, and measures such as distance do not provide as clear information as we might expect.There are three main areas where complex high dimensionality and large datasets arise naturally: data collected by online retailers, preference sites, and social media sites, and customer relationship databases, where there are large but sparse records available for each individual; data derived from text and speech, where the attributes are words and so the corresponding datasets are wide, and sparse; and data collected for security, defense, law enforcement, and intelligence purposes, where the datasets arelarge and wide. Such datasets are usually understood either by finding the set of clusters they contain or by looking for the outliers, but these strategies conceal subtleties that are often ignored. In this book the author suggests new ways of thinking about high-dimensional spaces using two models: a skeleton that relates the clusters to one another; and boundaries in the empty space between clusters that provide new perspectives on outliers and on outlying regions.The book will be of value to practitioners, graduate students and researchers.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 120 pp. Englisch.
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Understanding High-Dimensional Spaces
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Understanding High-Dimensional Spaces
Published by Springer Berlin Heidelberg Sep 2012, 2012
ISBN 10: 3642333974 ISBN 13: 9783642333972
Language: English
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values. Such spaces are not easy to work with because of their high dimensionality: our intuition about space is not reliable, and measures such as distance do not provide as clear information as we might expect. There are three main areas where complex high dimensionality and large datasets arise naturally: data collected by online retailers, preference sites, and social media sites, and customer relationship databases, where there are large but sparse records available for each individual; data derived from text and speech, where the attributes are words and so the corresponding datasets are wide, and sparse; and data collected for security, defense, law enforcement, and intelligence purposes, where the datasets are large and wide. Such datasets are usually understood either by finding the set of clusters they contain or by looking for the outliers, but these strategies conceal subtleties that are often ignored. In this book the author suggests new ways of thinking about high-dimensional spaces using two models: a skeleton that relates the clusters to one another; and boundaries in the empty space between clusters that provide new perspectives on outliers and on outlying regions. The book will be of value to practitioners, graduate students and researchers. 120 pp. Englisch.
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Understanding High-Dimensional Spaces
Published by Springer Berlin Heidelberg, 2012
ISBN 10: 3642333974 ISBN 13: 9783642333972
Language: English
£ 41.91
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. High-dimensional spaces arise naturally as a way of modelling datasets with many attributes Author suggests new ways of thinking about high-dimensional spaces using two models Valuable for practitioners, graduate students and researchers.
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Condition: New. Print on Demand pp. 120 29 Illus.
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Add to basketCondition: New. PRINT ON DEMAND pp. 120.
