000 03657nam a22005295i 4500
001 978-3-642-33398-9
003 DE-He213
005 20140220083327.0
007 cr nn 008mamaa
008 120928s2012 gw | s |||| 0|eng d
020 _a9783642333989
_9978-3-642-33398-9
024 7 _a10.1007/978-3-642-33398-9
_2doi
050 4 _aQA75.5-76.95
072 7 _aUT
_2bicssc
072 7 _aCOM069000
_2bisacsh
072 7 _aCOM032000
_2bisacsh
082 0 4 _a005.7
_223
100 1 _aSkillicorn, David B.
_eauthor.
245 1 0 _aUnderstanding High-Dimensional Spaces
_h[electronic resource] /
_cby David B. Skillicorn.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2012.
300 _aIX, 108 p. 29 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5768
505 0 _aIntroduction -- Basic Structure of High-Dimensional Spaces -- Algorithms -- Spaces with a Single Center -- Spaces with Multiple Clusters -- Representation by Graphs -- Using Models of High-Dimensional Spaces -- Including Contextual Information -- Conclusions -- Index -- References.
520 _aHigh-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 0 _aComputer science.
650 0 _aData protection.
650 0 _aData structures (Computer science).
650 0 _aInformation systems.
650 0 _aElectronic data processing.
650 1 4 _aComputer Science.
650 2 4 _aInformation Systems and Communication Service.
650 2 4 _aData Structures, Cryptology and Information Theory.
650 2 4 _aComputing Methodologies.
650 2 4 _aSystems and Data Security.
650 2 4 _ae-Commerce/e-business.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642333972
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-33398-9
912 _aZDB-2-SCS
999 _c103630
_d103630