000 | 03570nam a22005175i 4500 | ||
---|---|---|---|
001 | 978-1-4471-6793-8 | ||
003 | DE-He213 | ||
005 | 20200421112224.0 | ||
007 | cr nn 008mamaa | ||
008 | 160324s2016 xxk| s |||| 0|eng d | ||
020 |
_a9781447167938 _9978-1-4471-6793-8 |
||
024 | 7 |
_a10.1007/978-1-4471-6793-8 _2doi |
|
050 | 4 | _aQA76.9.D343 | |
072 | 7 |
_aUNF _2bicssc |
|
072 | 7 |
_aUYQE _2bicssc |
|
072 | 7 |
_aCOM021030 _2bisacsh |
|
082 | 0 | 4 |
_a006.312 _223 |
100 | 1 |
_aLerman, Isra�el C�esar. _eauthor. |
|
245 | 1 | 0 |
_aFoundations and Methods in Combinatorial and Statistical Data Analysis and Clustering _h[electronic resource] / _cby Isra�el C�esar Lerman. |
250 | _a1st ed. 2016. | ||
264 | 1 |
_aLondon : _bSpringer London : _bImprint: Springer, _c2016. |
|
300 |
_aXXIV, 647 p. 54 illus. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
||
337 |
_acomputer _bc _2rdamedia |
||
338 |
_aonline resource _bcr _2rdacarrier |
||
347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aAdvanced Information and Knowledge Processing, _x1610-3947 |
|
505 | 0 | _aPreface -- On Some Facets of the Partition Set of a Finite Set -- Two Methods of Non-hierarchical Clustering -- Structure and Mathematical Representation of Data -- Ordinal and Metrical Analysis of the Resemblance Notion -- Comparing Attributes by a Probabilistic and Statistical Association I -- Comparing Attributes by a Probabilistic and Statistical Association II -- Comparing Objects or Categories Described by Attributes -- The Notion of "Natural" Class, Tools for its Interpretation. The Classifiability Concept -- Quality Measures in Clustering -- Building a Classification Tree -- Applying the LLA Method to Real Data -- Conclusion and Thoughts for Future Works. | |
520 | _aThis book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field. With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial and statistical. Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages: Clustering a set of descriptive attributes Clustering a set of objects or a set of object categories Establishing correspondence between these two dual clusterings Tools for interpreting the reasons of a given cluster or clustering are also included. < Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aData mining. | |
650 | 0 | _aCombinatorics. | |
650 | 0 | _aStatistics. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aStatistics and Computing/Statistics Programs. |
650 | 2 | 4 | _aCombinatorics. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9781447167914 |
830 | 0 |
_aAdvanced Information and Knowledge Processing, _x1610-3947 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-1-4471-6793-8 |
912 | _aZDB-2-SCS | ||
942 | _cEBK | ||
999 |
_c57599 _d57599 |