Relational Data Mining
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Data Mining In Finance
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Author : Boris Kovalerchuk
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-11
Data Mining In Finance written by Boris Kovalerchuk and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-11 with Computers categories.
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.
Relational Data Mining
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Author : Saso Dzeroski
language : en
Publisher: Springer Science & Business Media
Release Date : 2001-08
Relational Data Mining written by Saso Dzeroski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08 with Business & Economics categories.
As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the area. The first part introduces the reader to the basics and principles of classical knowledge discovery in databases and inductive logic programming; subsequent chapters by leading experts assess the techniques in relational data mining in a principled and comprehensive way; finally, three chapters deal with advanced applications in various fields and refer the reader to resources for relational data mining. This book will become a valuable source of reference for R&D professionals active in relational data mining. Students as well as IT professionals and ambitioned practitioners interested in learning about relational data mining will appreciate the book as a useful text and gentle introduction to this exciting new field.
Multi Relational Data Mining
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Author : Arno J. Knobbe
language : en
Publisher:
Release Date : 2006
Multi Relational Data Mining written by Arno J. Knobbe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Data mining categories.
With the increased possibilities in modern society for companies and institutions to gather data cheaply and efficiently, the subject of Data Mining has become of increasing importance. This work looks into the different uses of Data Mining, covering the subject of Multi-Relational Data Mining (MRDM), the approach to Structured Data Mining.
Logical And Relational Learning
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Author : Luc De Raedt
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-27
Logical And Relational Learning written by Luc De Raedt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-27 with Computers categories.
This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.
Multi Relational Data Mining
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Author : B.L.J. Kaczmarek
language : en
Publisher: IOS Press
Release Date : 2006-08-25
Multi Relational Data Mining written by B.L.J. Kaczmarek and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-08-25 with Computers categories.
With the increased possibilities in modern society for companies and institutions to gather data cheaply and efficiently, the subject of Data Mining has become of increasing importance. This interest has inspired a rapidly maturing research field with developments both on a theoretical, as well as on a practical level with the availability of a range of commercial tools. Unfortunately, the widespread application of this technology has been limited by an important assumption in mainstream Data Mining approaches. This assumption – all data resides, or can be made to reside, in a single table – prevents the use of these Data Mining tools in certain important domains, or requires considerable massaging and altering of the data as a pre-processing step. This limitation has spawned a relatively recent interest in richer Data Mining paradigms that do allow structured data as opposed to the traditional flat representation. This publication goes into the different uses of Data Mining, with Multi-Relational Data Mining (MRDM), the approach to Structured Data Mining, as the main subject of this book.
Relational Data Clustering
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Author : Bo Long
language : en
Publisher: CRC Press
Release Date : 2010-05-19
Relational Data Clustering written by Bo Long and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-05-19 with Business & Economics categories.
A culmination of the authors' years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems. After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering: Clustering on bi-type heterogeneous relational data Multi-type heterogeneous relational data Homogeneous relational data clustering Clustering on the most general case of relational data Individual relational clustering framework Recent research on evolutionary clustering This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.
Mining Very Large Databases With Parallel Processing
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Author : Alex A. Freitas
language : en
Publisher: Springer Science & Business Media
Release Date : 1997-11-30
Mining Very Large Databases With Parallel Processing written by Alex A. Freitas and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-11-30 with Computers categories.
Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. It is an interdisciplinary text, describing advances in the integration of three computer science areas, namely `intelligent' (machine learning-based) data mining techniques, relational databases and parallel processing. The basic idea is to use concepts and techniques of the latter two areas - particularly parallel processing - to speed up and scale up data mining algorithms. The book is divided into three parts. The first part presents a comprehensive review of intelligent data mining techniques such as rule induction, instance-based learning, neural networks and genetic algorithms. Likewise, the second part presents a comprehensive review of parallel processing and parallel databases. Each of these parts includes an overview of commercially-available, state-of-the-art tools. The third part deals with the application of parallel processing to data mining. The emphasis is on finding generic, cost-effective solutions for realistic data volumes. Two parallel computational environments are discussed, the first excluding the use of commercial-strength DBMS, and the second using parallel DBMS servers. It is assumed that the reader has a knowledge roughly equivalent to a first degree (BSc) in accurate sciences, so that (s)he is reasonably familiar with basic concepts of statistics and computer science. The primary audience for Mining Very Large Databases with Parallel Processing is industry data miners and practitioners in general, who would like to apply intelligent data mining techniques to large amounts of data. The book will also be of interest to academic researchers and postgraduate students, particularly database researchers, interested in advanced, intelligent database applications, and artificial intelligence researchers interested in industrial, real-world applications of machine learning.
Advances In Knowledge Discovery And Data Mining
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Author : Thanaruk Theeramunkong
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-04-20
Advances In Knowledge Discovery And Data Mining written by Thanaruk Theeramunkong and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-20 with Computers categories.
This book constitutes the refereed proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, held in Bangkok, Thailand, in April 2009. The 39 revised full papers and 73 revised short papers presented together with 3 keynote talks were carefully reviewed and selected from 338 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scientific discovery, data visualization, causal induction, and knowledge-based systems.
Content Addressable Memories
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Author : Teuvo Kohonen
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Content Addressable Memories written by Teuvo Kohonen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Due to continual progress in the large-scale integration of semiconductor circuits, parallel computing principles can already be met in low-cost sys tems: numerous examples exist in image processing, for which special hard ware is implementable with quite modest resources even by nonprofessional designers. Principles of content addressing, if thoroughly understood, can thereby be applied effectively using standard components. On the other hand, mass storage based on associative principles still exists only in the long term plans of computer technologists. This situation is somewhat confused by the fact that certain expectations are held for the development of new storage media such as optical memories and "spin glasses" (metal alloys with low-density magnetic impurities). Their technologies, however, may not ripen until after "fifth generation" computers have been built. It seems that software methods for content addressing, especially those based on hash coding principles, are still holding their position firmly, and a few innovations have been developed recently. As they need no special hardware, one might expect that they will spread to a wide circle of users. This monograph is based on an extensive literature survey, most of which was published in the First Edition. I have added Chap. ?, which contains a review of more recent work. This updated book now has references to over 1200 original publications. In the editing of the new material, I received valuable help from Anneli HeimbUrger, M. Sc. , and Mrs. Leila Koivisto.