000 | 03650nam a22005775i 4500 | ||
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001 | 978-3-030-92694-6 | ||
003 | DE-He213 | ||
005 | 20240423125416.0 | ||
007 | cr nn 008mamaa | ||
008 | 220304s2022 sz | s |||| 0|eng d | ||
020 |
_a9783030926946 _9978-3-030-92694-6 |
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024 | 7 |
_a10.1007/978-3-030-92694-6 _2doi |
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050 | 4 | _aQA76.9.D343 | |
072 | 7 |
_aUNF _2bicssc |
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072 | 7 |
_aUYQE _2bicssc |
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_aCOM021030 _2bisacsh |
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_aUNF _2thema |
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072 | 7 |
_aUYQE _2thema |
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082 | 0 | 4 |
_a006.312 _223 |
100 | 1 |
_aLerman, Israël César. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aSeriation in Combinatorial and Statistical Data Analysis _h[electronic resource] / _cby Israël César Lerman, Henri Leredde. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022. |
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300 |
_aXIV, 277 p. 114 illus., 6 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aAdvanced Information and Knowledge Processing, _x2197-8441 |
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505 | 0 | _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. | |
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. | ||
650 | 0 | _aData mining. | |
650 | 0 |
_aComputer science _xMathematics. |
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650 | 0 | _aMachine learning. | |
650 | 1 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aMathematics of Computing. |
650 | 2 | 4 | _aMachine Learning. |
700 | 1 |
_aLeredde, Henri. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030926939 |
776 | 0 | 8 |
_iPrinted edition: _z9783030926953 |
776 | 0 | 8 |
_iPrinted edition: _z9783030926960 |
830 | 0 |
_aAdvanced Information and Knowledge Processing, _x2197-8441 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-92694-6 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c177618 _d177618 |