Last edited by Meztigal
Tuesday, August 4, 2020 | History

2 edition of Relational Data Mining found in the catalog.

Relational Data Mining

by SaЕЎo DЕѕeroski

  • 87 Want to read
  • 18 Currently reading

Published by Springer Berlin Heidelberg in Berlin, Heidelberg .
Written in English

    Subjects:
  • Data mining,
  • Computer science,
  • Database management,
  • Optical pattern recognition,
  • Information storage and retrieval systems,
  • Management information systems,
  • Artificial intelligence

  • About the Edition

    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.

    Edition Notes

    Statementedited by Sašo Džeroski, Nada Lavrač
    ContributionsLavrač, Nada
    Classifications
    LC ClassificationsQA76.9.D343
    The Physical Object
    Format[electronic resource] /
    Pagination1 online resource (xix, 398 p.)
    Number of Pages398
    ID Numbers
    Open LibraryOL27085121M
    ISBN 103642076041, 3662045990
    ISBN 109783642076046, 9783662045992
    OCLC/WorldCa851381929

    As the first book devoted to relational data mining, this coherently written multi-author monograph gives a radical introduction and systematic overview of the world. 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.

    It also briefly introduces relational data mining, starting with patterns that involve multiple relations and laying down the basic principles common to relational data mining algorithms. An overview of the contents of this book is given, as well as pointers to literature and Internet resources on data mining. Data Mining: Types of Data • Relational data and transactional data • Spatial and temporal data, spatio-temporal observations • Time-series data • Text • Images, video • Mixtures of data • Sequence data • Features from processing other data sources Ramakrishnan and Gehrke.

    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. Multi-Relational Data Mining or MRDM is a growing research area focuses on discovering hidden patterns and useful knowledge from relational databases. While the vast majority of data mining algorithms and techniques look for patterns in a flat single-table data representation, the sub-domain of Author: Ali H. Gazala, Waseem Ahmad.


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Relational Data Mining by SaЕЎo DЕѕeroski Download PDF EPUB FB2

As the first book devoted to relational data mining, this coherently written multi-author monograph provides a thorough introduction and systematic overview of the : Paperback.

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 Cited by: 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. Relational Data Clustering: Models, Algorithms, and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series Book 14) - Kindle edition by Long, Bo, Zhang, Zhongfei, Yu, Philip S.

Download it once and read it on your Kindle device, PC, phones or by: 4. Data Mining in Finance: Advances In Relational And Hybrid Methods (The Springer International Series in Engineering and Computer Science) [Kovalerchuk, Boris] on *FREE* shipping on qualifying offers.

Data Mining in Finance: Advances In Relational And Hybrid Methods (The Springer International Series in Engineering and Computer Science)/5(4). This 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.

This book is a clear introduction to relational data mining methods, with a focus on supervised learning, which makes use of training models. The text is enriched with excellent state-of-the-practice comments on relational database and fuzzy logic techniques. 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. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools.

Advanced topics including big data analytics, relational data models and NoSQL are discussed in : Parteek Bhatia. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability.

While most existing Data Mining approaches look for patterns in a single data table, relational Data Mining (RDM) approaches look for patterns that involve multiple tables (relations) from a relational by: Database Systems by Raj Sunderraman. This note covers the following topics: Databases and database users, database system concepts and architecture, relational data model, lab manual, the relational data model and constraints, Relational algebra and calculus, sql, basic sql, JDBC API to access relational databases in java, JDBC API javadoc, sun JDBC, JDBC scrollable resultsets, oracle JDBC.

Introduction 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. 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.

This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data including stream data, sequence data, graph structured data, social network data, and multi-relational data. Granular-Relational Data Mining: How to Mine Relational Data in the Paradigm of Granular Computing.

(Studies in Computational Intelligence) [Piotr Hońko] on *FREE* shipping on qualifying offers. This 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.

: Abstract Data Mining algorithms look for patterns in data. While most existing Data Mining approaches look for patterns in a single data table, relational Data Mining (RDM) approaches look for patterns that involve multiple tables (relations) from a relational database.

In recent years, the most common types of patterns and. A data mining solution can be based either on multidimensional data-that is, an existing cube-or on purely relational data, such as the tables and views in a data warehouse, or on text files, Excel workbooks, or other external data sources.

You can create data mining objects within an existing multidimensional database solution. A relational database is a digital database based on the relational model of data, as proposed by E.

Codd in A software system used to maintain relational databases is a relational database management system (RDBMS). Many relational database systems have an option of using the SQL (Structured Query Language) for querying and maintaining the database.

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.

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.[Show full abstract] relational data mining context that enable effective and robust reasoning about relational data structures.

Even though a panoply of works have focused, separately, on.Download PDF Data Mining book full free. Data Mining available for download and read online in other formats.

PDF Book Download Full PDF eBook Free Download social network data, and multi-relational data. A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data Updates that.