| 000 | 03734nam a22005175i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-52751-2 | ||
| 003 | DE-He213 | ||
| 005 | 20220801215150.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 170203s2017 sz | s |||| 0|eng d | ||
| 020 |
_a9783319527512 _9978-3-319-52751-2 |
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| 024 | 7 |
_a10.1007/978-3-319-52751-2 _2doi |
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| 050 | 4 | _aQ342 | |
| 072 | 7 |
_aUYQ _2bicssc |
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_aTEC009000 _2bisacsh |
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_a006.3 _223 |
| 100 | 1 |
_aHońko, Piotr. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _942727 |
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| 245 | 1 | 0 |
_aGranular-Relational Data Mining _h[electronic resource] : _bHow to Mine Relational Data in the Paradigm of Granular Computing? / _cby Piotr Hońko. |
| 250 | _a1st ed. 2017. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017. |
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| 300 |
_aXV, 123 p. 4 illus. _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 |
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| 490 | 1 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v702 |
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| 505 | 0 | _aPreface -- Chapter 1: Introduction -- Part I: Generalized Related Set Based Approach -- Chapter 2: Information System for Relational Data -- Chapter 3: Properties of Granular-Relational Data Mining Framework -- Chapter 4: Association Discovery and Classification Rule Mining -- Chapter 5: Rough-Granular Computing -- Part II: Description Language Based Approach -- Chapter 6: Compound Information Systems -- Chapter 7: From Granular-Data Mining Framework to its Relational Version -- Chapter 8: Relation-Based Granules -- Chapter 9: Compound Approximation Spaces -- Conclusions -- References -- Index. | |
| 520 | _aThis book provides two general granular computing approaches to mining relational data, the first of which uses abstract descriptions of relational objects to build their granular representation, while the second extends existing granular data mining solutions to a relational case. Both approaches make it possible to perform and improve popular data mining tasks such as classification, clustering, and association discovery. How can different relational data mining tasks best be unified? How can the construction process of relational patterns be simplified? How can richer knowledge from relational data be discovered? All these questions can be answered in the same way: by mining relational data in the paradigm of granular computing! This book will allow readers with previous experience in the field of relational data mining to discover the many benefits of its granular perspective. In turn, those readers familiar with the paradigm of granular computing will find valuable insights on its application to mining relational data. Lastly, the book offers all readers interested in computational intelligence in the broader sense the opportunity to deepen their understanding of the newly emerging field granular-relational data mining. | ||
| 650 | 0 |
_aComputational intelligence. _97716 |
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| 650 | 0 |
_aArtificial intelligence. _93407 |
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| 650 | 1 | 4 |
_aComputational Intelligence. _97716 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
| 710 | 2 |
_aSpringerLink (Online service) _942728 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319527505 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319527529 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319849775 |
| 830 | 0 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v702 _942729 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-319-52751-2 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c77179 _d77179 |
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