Data Mining in Finance: Advances in Relational and Hybrid Methods
Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book fo...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2002
|
| Schriftenreihe: | The International Series in Engineering and Computer Science
547 |
| Schlagworte: | |
| Online-Zugang: | FHI01 BTU01 Volltext |
| Zusammenfassung: | Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for 'mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics |
| Beschreibung: | 1 Online-Ressource (XVI, 308 p) |
| ISBN: | 9780306470189 |
| DOI: | 10.1007/b116453 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV045148425 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 180827s2002 |||| o||u| ||||||eng d | ||
| 020 | |a 9780306470189 |9 978-0-306-47018-9 | ||
| 024 | 7 | |a 10.1007/b116453 |2 doi | |
| 035 | |a (ZDB-2-ENG)978-0-306-47018-9 | ||
| 035 | |a (OCoLC)1050940214 | ||
| 035 | |a (DE-599)BVBBV045148425 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-573 |a DE-634 | ||
| 082 | 0 | |a 005.74 |2 23 | |
| 084 | |a QP 700 |0 (DE-625)141926: |2 rvk | ||
| 084 | |a ST 610 |0 (DE-625)143683: |2 rvk | ||
| 100 | 1 | |a Kovalerchuk, Boris |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Data Mining in Finance |b Advances in Relational and Hybrid Methods |c by Boris Kovalerchuk, Evgenii Vityaev |
| 264 | 1 | |a Boston, MA |b Springer US |c 2002 | |
| 300 | |a 1 Online-Ressource (XVI, 308 p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a The International Series in Engineering and Computer Science |v 547 | |
| 520 | |a Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for 'mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics | ||
| 650 | 4 | |a Computer Science | |
| 650 | 4 | |a Data Structures, Cryptology and Information Theory | |
| 650 | 4 | |a Artificial Intelligence (incl. Robotics) | |
| 650 | 4 | |a Finance, general | |
| 650 | 4 | |a Computer science | |
| 650 | 4 | |a Finance | |
| 650 | 4 | |a Data structures (Computer science) | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 0 | 7 | |a Finanzmanagement |0 (DE-588)4139075-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Finanzierung |0 (DE-588)4017182-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Kursprognose |0 (DE-588)4418886-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Investitionsanalyse |0 (DE-588)4273190-2 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Finanzmanagement |0 (DE-588)4139075-1 |D s |
| 689 | 0 | 1 | |a Kursprognose |0 (DE-588)4418886-9 |D s |
| 689 | 0 | 2 | |a Investitionsanalyse |0 (DE-588)4273190-2 |D s |
| 689 | 0 | 3 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 689 | 1 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 1 | 1 | |a Finanzierung |0 (DE-588)4017182-6 |D s |
| 689 | 1 | |8 2\p |5 DE-604 | |
| 700 | 1 | |a Vityaev, Evgenii |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9780792378044 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/b116453 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-2-ENG | ||
| 940 | 1 | |q ZDB-2-ENG_2000/2004 | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-030538124 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 2\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 966 | e | |u https://doi.org/10.1007/b116453 |l FHI01 |p ZDB-2-ENG |q ZDB-2-ENG_2000/2004 |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1007/b116453 |l BTU01 |p ZDB-2-ENG |q ZDB-2-ENG_Archiv |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804178818385051648 |
|---|---|
| any_adam_object | |
| author | Kovalerchuk, Boris Vityaev, Evgenii |
| author_facet | Kovalerchuk, Boris Vityaev, Evgenii |
| author_role | aut aut |
| author_sort | Kovalerchuk, Boris |
| author_variant | b k bk e v ev |
| building | Verbundindex |
| bvnumber | BV045148425 |
| classification_rvk | QP 700 ST 610 |
| collection | ZDB-2-ENG |
| ctrlnum | (ZDB-2-ENG)978-0-306-47018-9 (OCoLC)1050940214 (DE-599)BVBBV045148425 |
| dewey-full | 005.74 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.74 |
| dewey-search | 005.74 |
| dewey-sort | 15.74 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Wirtschaftswissenschaften |
| doi_str_mv | 10.1007/b116453 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04507nmm a2200697zcb4500</leader><controlfield tag="001">BV045148425</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">180827s2002 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780306470189</subfield><subfield code="9">978-0-306-47018-9</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/b116453</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-2-ENG)978-0-306-47018-9</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1050940214</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV045148425</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-573</subfield><subfield code="a">DE-634</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">005.74</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QP 700</subfield><subfield code="0">(DE-625)141926:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 610</subfield><subfield code="0">(DE-625)143683:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kovalerchuk, Boris</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data Mining in Finance</subfield><subfield code="b">Advances in Relational and Hybrid Methods</subfield><subfield code="c">by Boris Kovalerchuk, Evgenii Vityaev</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boston, MA</subfield><subfield code="b">Springer US</subfield><subfield code="c">2002</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XVI, 308 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">The International Series in Engineering and Computer Science</subfield><subfield code="v">547</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for 'mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer Science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data Structures, Cryptology and Information Theory</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial Intelligence (incl. Robotics)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Finance, general</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Computer science</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Finance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Data structures (Computer science)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Finanzmanagement</subfield><subfield code="0">(DE-588)4139075-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Finanzierung</subfield><subfield code="0">(DE-588)4017182-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Kursprognose</subfield><subfield code="0">(DE-588)4418886-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Investitionsanalyse</subfield><subfield code="0">(DE-588)4273190-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Finanzmanagement</subfield><subfield code="0">(DE-588)4139075-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Kursprognose</subfield><subfield code="0">(DE-588)4418886-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Investitionsanalyse</subfield><subfield code="0">(DE-588)4273190-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="1"><subfield code="a">Finanzierung</subfield><subfield code="0">(DE-588)4017182-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Vityaev, Evgenii</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9780792378044</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/b116453</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-ENG</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-ENG_2000/2004</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-030538124</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/b116453</subfield><subfield code="l">FHI01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="q">ZDB-2-ENG_2000/2004</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1007/b116453</subfield><subfield code="l">BTU01</subfield><subfield code="p">ZDB-2-ENG</subfield><subfield code="q">ZDB-2-ENG_Archiv</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV045148425 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:01Z |
| institution | BVB |
| isbn | 9780306470189 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030538124 |
| oclc_num | 1050940214 |
| open_access_boolean | |
| owner | DE-573 DE-634 |
| owner_facet | DE-573 DE-634 |
| physical | 1 Online-Ressource (XVI, 308 p) |
| psigel | ZDB-2-ENG ZDB-2-ENG_2000/2004 ZDB-2-ENG ZDB-2-ENG_2000/2004 ZDB-2-ENG ZDB-2-ENG_Archiv |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | Springer US |
| record_format | marc |
| series2 | The International Series in Engineering and Computer Science |
| spelling | Kovalerchuk, Boris Verfasser aut Data Mining in Finance Advances in Relational and Hybrid Methods by Boris Kovalerchuk, Evgenii Vityaev Boston, MA Springer US 2002 1 Online-Ressource (XVI, 308 p) txt rdacontent c rdamedia cr rdacarrier The International Series in Engineering and Computer Science 547 Data Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for 'mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Finance, general Computer science Finance Data structures (Computer science) Artificial intelligence Finanzmanagement (DE-588)4139075-1 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Finanzierung (DE-588)4017182-6 gnd rswk-swf Kursprognose (DE-588)4418886-9 gnd rswk-swf Investitionsanalyse (DE-588)4273190-2 gnd rswk-swf Finanzmanagement (DE-588)4139075-1 s Kursprognose (DE-588)4418886-9 s Investitionsanalyse (DE-588)4273190-2 s Data Mining (DE-588)4428654-5 s 1\p DE-604 Finanzierung (DE-588)4017182-6 s 2\p DE-604 Vityaev, Evgenii aut Erscheint auch als Druck-Ausgabe 9780792378044 https://doi.org/10.1007/b116453 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Kovalerchuk, Boris Vityaev, Evgenii Data Mining in Finance Advances in Relational and Hybrid Methods Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Finance, general Computer science Finance Data structures (Computer science) Artificial intelligence Finanzmanagement (DE-588)4139075-1 gnd Data Mining (DE-588)4428654-5 gnd Finanzierung (DE-588)4017182-6 gnd Kursprognose (DE-588)4418886-9 gnd Investitionsanalyse (DE-588)4273190-2 gnd |
| subject_GND | (DE-588)4139075-1 (DE-588)4428654-5 (DE-588)4017182-6 (DE-588)4418886-9 (DE-588)4273190-2 |
| title | Data Mining in Finance Advances in Relational and Hybrid Methods |
| title_auth | Data Mining in Finance Advances in Relational and Hybrid Methods |
| title_exact_search | Data Mining in Finance Advances in Relational and Hybrid Methods |
| title_full | Data Mining in Finance Advances in Relational and Hybrid Methods by Boris Kovalerchuk, Evgenii Vityaev |
| title_fullStr | Data Mining in Finance Advances in Relational and Hybrid Methods by Boris Kovalerchuk, Evgenii Vityaev |
| title_full_unstemmed | Data Mining in Finance Advances in Relational and Hybrid Methods by Boris Kovalerchuk, Evgenii Vityaev |
| title_short | Data Mining in Finance |
| title_sort | data mining in finance advances in relational and hybrid methods |
| title_sub | Advances in Relational and Hybrid Methods |
| topic | Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Finance, general Computer science Finance Data structures (Computer science) Artificial intelligence Finanzmanagement (DE-588)4139075-1 gnd Data Mining (DE-588)4428654-5 gnd Finanzierung (DE-588)4017182-6 gnd Kursprognose (DE-588)4418886-9 gnd Investitionsanalyse (DE-588)4273190-2 gnd |
| topic_facet | Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Finance, general Computer science Finance Data structures (Computer science) Artificial intelligence Finanzmanagement Data Mining Finanzierung Kursprognose Investitionsanalyse |
| url | https://doi.org/10.1007/b116453 |
| work_keys_str_mv | AT kovalerchukboris datamininginfinanceadvancesinrelationalandhybridmethods AT vityaevevgenii datamininginfinanceadvancesinrelationalandhybridmethods |