{"product_id":"9780792378044","title":"The Springer International Series in Engineering and Computer Science: Advances in Relational and Hybrid Methods","description":"\u003ch1\u003eThe Springer International Series in Engineering and Computer Science: Advances in Relational and Hybrid Methods\u003c\/h1\u003e \u003ch2\u003eKovalerchuk, Boris; Vityaev, Evgenii\u003c\/h2\u003e \u003cp\u003e\u003cem\u003eData Mining in Finance\u003c\/em\u003e 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. \u003cbr\u003e  \u003cem\u003eData Mining in Finance\u003c\/em\u003e 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. \u003cbr\u003e  \u003cem\u003eData Mining in Finance\u003c\/em\u003e contains a number of practical examples  of forecasting S\u0026amp;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.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2000-04-30\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9780792378044\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/b116453\u003c\/p\u003e \u003cp\u003eDimensions: 234cm x156cm\u003c\/p\u003e \u003cp\u003ePages: 308\u003c\/p\u003e ","brand":"Springer US","offers":[{"title":"Default Title","offer_id":44697969688716,"sku":"9780792378044","price":197.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9780792378044.jpg?v=1777981047","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9780792378044","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}