LEADER 04497nam 22007094a 450 001 9910780039903321 005 20200520144314.0 010 $a1-280-20603-9 010 $a9786610206032 010 $a0-306-47018-7 024 7 $a10.1007/b116453 035 $a(CKB)111056486607662 035 $a(EBL)3035690 035 $a(SSID)ssj0000135022 035 $a(PQKBManifestationID)11143483 035 $a(PQKBTitleCode)TC0000135022 035 $a(PQKBWorkID)10057692 035 $a(PQKB)10720684 035 $a(DE-He213)978-0-306-47018-9 035 $a(MiAaPQ)EBC3035690 035 $a(MiAaPQ)EBC196911 035 $a(Au-PeEL)EBL3035690 035 $a(CaPaEBR)ebr10053006 035 $a(CaONFJC)MIL20603 035 $a(OCoLC)559414525 035 $a(Au-PeEL)EBL196911 035 $a(PPN)237929457 035 $a(EXLCZ)99111056486607662 100 $a20000207d2000 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData mining in finance$b[electronic resource] $eadvances in relational and hybrid methods /$fby Boris Kovalerchuk and Evgenii Vityaev 205 $a1st ed. 2000. 210 $aBoston $cKluwer Academic Publishers ;$aNorwell, Mass $cDistributors for North, Central, and South America, Kluwer Academic Publishers$dc2000 215 $a1 online resource (325 p.) 225 1 $aThe Kluwer international series in engineering and computer science ;$vSECS 547 300 $aDescription based upon print version of record. 311 $a0-7923-7804-0 320 $aIncludes bibliographical references and index. 320 $aIncludes bibliographical references (p. [285]-298) and index. 327 $aThe scope and methods of the study -- Numerical Data Mining Models and Financial Applications -- Rule-Based and Hybrid Financial Data Mining -- Relational Data Mining (RDM) -- Financial Applications of Relational Data Mining -- Comparison of Performance of RDM and other methods in financial applications -- Fuzzy logic approach and its financial applications. 330 $aData Mining in Finance presents a comprehensive overview of major algorithmic approaches to predictive data mining, including statistical, neural networks, ruled-based, decision-tree, and fuzzy-logic methods, and then examines the suitability of these approaches to financial data mining. The book focuses specifically on relational data mining (RDM), which is a learning method able to learn more expressive rules than other symbolic approaches. RDM is thus better suited for financial mining, because it is able to make greater use of underlying domain knowledge. Relational data mining also has a better ability to explain the discovered rules - an ability critical for avoiding spurious patterns which inevitably arise when the number of variables examined is very large. The earlier algorithms for relational data mining, also known as inductive logic programming (ILP), suffer from a relative computational inefficiency and have rather limited tools for processing numerical data. Data Mining in Finance introduces a new approach, combining relational data mining with the analysis of statistical significance of discovered rules. This reduces the search space and speeds up the algorithms. The book also presents interactive and fuzzy-logic tools for `mining' the knowledge from the experts, further reducing the search space. Data Mining in Finance contains a number of practical examples of forecasting S&P 500, exchange rates, stock directions, and rating stocks for portfolio, allowing interested readers to start building their own models. This book is an excellent reference for researchers and professionals in the fields of artificial intelligence, machine learning, data mining, knowledge discovery, and applied mathematics. 410 0$aKluwer international series in engineering and computer science ;$vSECS 547. 606 $aInvestments$xData processing 606 $aStock price forecasting$xData processing 606 $aData mining 615 0$aInvestments$xData processing. 615 0$aStock price forecasting$xData processing. 615 0$aData mining. 676 $a332.1/0285/63 700 $aKovalerchuk$b Boris$0846538 701 $aVityaev$b Evgenii$01569404 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910780039903321 996 $aData mining in finance$93842322 997 $aUNINA