| 000 -LEADER |
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| fixed length control field |
nam a22 7a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
190220b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783642333972 |
| Terms of availability |
(pbk) |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.312 |
| Item number |
SKI |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Skillicorn, David B. |
| 245 ## - TITLE STATEMENT |
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| Title |
Understanding high-dimensional spaces |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
New York : |
| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2012 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
ix, 108 p. : |
| Other physical details |
ill. ; |
| Dimensions |
23.4 cm. |
| 365 ## - TRADE PRICE |
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| Price type code |
EURO |
| Price amount |
54.99 |
| Unit of pricing |
00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data mining |
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| Topical term or geographic name as entry element |
Data structures |
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| Topical term or geographic name as entry element |
Computer science |
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| Topical term or geographic name as entry element |
Data protection |
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| Topical term or geographic name as entry element |
Information systems |
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| Topical term or geographic name as entry element |
Electronic data processing |
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| Topical term or geographic name as entry element |
Computing Methodologies |
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| Topical term or geographic name as entry element |
Data Security |
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| Topical term or geographic name as entry element |
e-Commerce |
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| Topical term or geographic name as entry element |
Communication Service |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Item type |
Books |