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001444980 001__ 1444980
001444980 003__ OCoLC
001444980 005__ 20230310003806.0
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001444980 019__ $$a1302581271$$a1302692391$$a1302740549$$a1302951199$$a1302987032$$a1303050737$$a1303075769$$a1303185006$$a1303214679$$a1303554657
001444980 020__ $$a9783030926946$$q(electronic bk.)
001444980 020__ $$a303092694X$$q(electronic bk.)
001444980 020__ $$z9783030926939
001444980 020__ $$z3030926931
001444980 0247_ $$a10.1007/978-3-030-92694-6$$2doi
001444980 035__ $$aSP(OCoLC)1302330517
001444980 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dOCLCO$$dOCLCF$$dUKAHL$$dOCLCQ
001444980 049__ $$aISEA
001444980 050_4 $$aQA76.9.D343$$bL47 2022
001444980 08204 $$a006.3/12$$223
001444980 1001_ $$aLerman, Israël César,$$eauthor.
001444980 24510 $$aSeriation in combinatorial and statistical data analysis /$$cIsraël César Lerman, Henri Leredde.
001444980 264_1 $$aCham :$$bSpringer,$$c[2022]
001444980 264_4 $$c©2022
001444980 300__ $$a1 online resource :$$billustrations (some color).
001444980 336__ $$atext$$btxt$$2rdacontent
001444980 337__ $$acomputer$$bc$$2rdamedia
001444980 338__ $$aonline resource$$bcr$$2rdacarrier
001444980 4901_ $$aAdvanced information and knowledge processing
001444980 504__ $$aIncludes bibliographical references and index.
001444980 5050_ $$aPreface -- Acknowledgements -- General Introduction. Methods and History -- Seriation from Proximity Variance Analysis -- Main Approachs in Seriation. The Attraction Pole Case -- Comparing Geometrical and Ordinal Seriation Methods in Formal and Real Cases -- A New Family of Combinatorial Algorithms in Seriation -- Clustering Methods from Proximity Variance Analysis -- Conclusion and Developments.
001444980 506__ $$aAccess limited to authorized users.
001444980 520__ $$aThis monograph offers an original broad and very diverse exploration of the seriation domain in data analysis, together with building a specific relation to clustering. Relative to a data table crossing a set of objects and a set of descriptive attributes, the search for orders which correspond respectively to these two sets is formalized mathematically and statistically. State-of-the-art methods are created and compared with classical methods and a thorough understanding of the mutual relationships between these methods is clearly expressed. The authors distinguish two families of methods: Geometric representation methods Algorithmic and Combinatorial methods Original and accurate methods are provided in the framework for both families. Their basis and comparison is made on both theoretical and experimental levels. The experimental analysis is very varied and very comprehensive. Seriation in Combinatorial and Statistical Data Analysis has a unique character in the literature falling within the fields of Data Analysis, Data Mining and Knowledge Discovery. It will be a valuable resource for students and researchers in the latter fields.
001444980 588__ $$aDescription based on print version record.
001444980 650_0 $$aData mining$$xStatistical methods.
001444980 650_0 $$aCombinatorial analysis$$xData processing.
001444980 650_6 $$aAnalyse combinatoire$$xInformatique.
001444980 655_0 $$aElectronic books.
001444980 7001_ $$aLeredde, Henri,$$eauthor.
001444980 77608 $$iPrint version:$$aLERMAN, ISRAEL CESAR.$$tSERIATION IN COMBINATORIAL AND STATISTICAL DATA ANALYSIS.$$d[Place of publication not identified] : SPRINGER, 2022$$z3030926931$$w(OCoLC)1285162632
001444980 830_0 $$aAdvanced information and knowledge processing.
001444980 852__ $$bebk
001444980 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-92694-6$$zOnline Access$$91397441.1
001444980 909CO $$ooai:library.usi.edu:1444980$$pGLOBAL_SET
001444980 980__ $$aBIB
001444980 980__ $$aEBOOK
001444980 982__ $$aEbook
001444980 983__ $$aOnline
001444980 994__ $$a92$$bISE