1 edition of **Rough Sets and Data Mining** found in the catalog.

- 45 Want to read
- 4 Currently reading

Published
**1996** by Springer US in Boston, MA .

Written in English

*Rough Sets and Data Mining: Analysis of Imprecise Data* is an edited collection of research chapters on the most recent developments in rough set theory and data mining. The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information. The authors of these chapters have been careful to include fundamental research with explanations as well as coverage of rough set tools that can be used for mining data bases.

The contributing authors consist of some of the leading scholars in the fields of rough sets, data mining, machine learning and other areas of artificial intelligence. Among the list of contributors are Z. Pawlak, J Grzymala-Busse, K. Slowinski, and others.

*Rough Sets and Data Mining: Analysis of Imprecise Data* will be a useful reference work for rough set researchers, data base designers and developers, and for researchers new to the areas of data mining and rough sets.

**Edition Notes**

Statement | by T.Y. Lin, N. Cercone |

Contributions | Cercone, N. |

The Physical Object | |
---|---|

Format | [electronic resource] : |

Pagination | 1 online resource (452 pages) |

Number of Pages | 452 |

ID Numbers | |

Open Library | OL27086002M |

ISBN 10 | 1461314615 |

ISBN 10 | 9781461314615 |

OCLC/WorldCa | 840281386 |

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Rough sets: Introduction Recent extensions of rough Rough Sets and Data Mining book theory: Rough mereology Ontology-based rough sets have developed new methods for decomposition of large data sets, data mining in distributed and multi-agent systems, and granular Size: KB.

Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining.

The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information.4/5(1). x Rough set with tools for data mining x Applications of rough set theory; x Case Rough set with tools in dengue diagnosis.

Fundamental concepts Rough Sets Theory has been under continuous development for over years, and a growing number of researchers have became its interested in methodology. It is a formal theory.

Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining.

The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about. Rough Sets and Data Mining: Analysis of Imprecise Data is an edited collection of research chapters on the most recent developments in rough set theory and data mining.

The chapters in this work cover a range of topics that focus on discovering dependencies among data, and reasoning about vague, uncertain and imprecise information. ROUGH SETS AND DATA MINING Zdzisław Pawlak Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul.

Baltycka 5, 44 Gliwice, Poland ABSTRACT The paper gives basic ideas of rough set theory - a new approach to vague data analysis. The lower. Rough Sets and Data Mining book book consists of 20 chapters distributed into four sections, which focus in turn on a historical review of Professor Zdzisław Pawlak and rough set theory; a review of the theory of rough sets; the state of the art of rough set theory; and major developments in.

For the rough set theory, in the process of data mining, there are still a large number of problems need to be discussed, such as large data sets, efficient reduction algorithm, parallel computing, hybrid algorithm, by: Data Mining, Rough Sets and Granular Computing [Lin, Tsau Young, Yao, Yiyu Y., Zadeh, Lotfi A.] on *FREE* shipping on qualifying offers.

Data Mining, Rough Sets and Granular ComputingFormat: Hardcover. Another methodology which has high relevance to data mining and plays a central role in this volume is that of rough set theory.

Basically, rough set theory may be viewed as a branch of granular computing. However, its applications to data. Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, 11th International Conference, RSFDGrCToronto, Canada, May, Proceedings pp Haegil Hwang.

α-leve l sets, in: Rough Sets and Data Mining: Book. Full-text available The model proposes a synergistic combination of Rough Sets and Data Envelopment Analysis (DEA).Author: Yiyu Yao.

A rough set is a formal approximation of a crisp set in terms of a pair of sets that give the lower and upper approximation of the original set Learn more in: Rough Set-Based Neuro-Fuzzy System 8. The concept of rough, or approximation, set s was introduced by Pawlak, and is based on the single assumption that information is associated with.

Data Mining Based on Rough Sets: /ch The chapter is focused on the data mining aspect of the applications of rough set theory. Consequently, the theoretical part is minimized to emphasize theCited by: Based on the Eq.

(1) and the classifications above rough sets can be characterized by the size of the boundary region and structure. III. Data representation with RST The paper is based on data-mining-related techniques of the original rough set model. In the following section, some data mining techniques and applications used with RST are.

This book constitutes the refereed proceedings of the 13th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrCheld in Moscow, Russia in June The 49 revised full papers presented together with 5 invited and 2 tutorial papers were carefully reviewed and selected from a total of 83 submissions.

In this perspective, granular computing has a position of centrality in data mining. Another methodology which has high relevance to data mining and plays a central role in this volume is that of rough set theory. Basically, rough set theory may be viewed as a. Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications.

Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD).

Chapter 2 (Rough Sets and Reasoning from Data) presents the application of rough set concept to reason from data (data mining).

Chapter 3 (Rough Sets and Bayes’ Theorem) gives a new look on Bayes’ theorem and shows that Bayes’ rule can be used differently to that offered by classical Bayesian reasoning methodology.

Data mining has emerged as a discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decisionmaking. In this paper, the basic concepts of rough set theory and other aspects of data mining are introduced. The rough set theory offers a viable approach for extraction of decision rules from data by: Book Description.

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and.

In this chapter, we discuss the theory and foundational issues in data mining, describe data mining methods and algorithms, and review data mining applications. Since a major focus of this book is on rough sets and its applications to database mining, one full section is devoted to summarizing the state of ro.

Rough set theory, introduced by Zdzislaw Pawlak in the early s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance t. In this talk, we will present basic concepts of rough sets and its relationship to Dempster-Shafer's theory.

We will discuss how to apply these concepts to data analysis and machine learning. Finally, we will present a framework for distributed incremental data. Find many great new & used options and get the best deals for Lecture Notes in Computer Science: Rough Sets and Current Trends in Computing Vol.

First International Conference, RSCTC '98, Warsaw, Poland, JuneProceedings (, Paperback) at the best online prices at eBay. Free shipping for many products.

It can also be an excellent handbook for researchers in the area of data mining and data warehousing. The revised edition includes a comprehensive chapter on rough set theory. The rough set theory, which is a tool of sets and relations for studying imprecision, vagueness, and uncertainty in data analysis, is a relatively new mathematical and Price: $ Abstract: Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision making.

Data mining is a discipline that has an important contribution to data analysis, discovery of new meaningful knowledge, and autonomous decision by: 1. Practical Applications of Data Mining by Suh Get Practical Applications of Data Mining now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from + publishers. By Alex Ivanovs, CodeCondo, Data mining, data analysis, these are the two terms that very often make the impressions of being very hard to understand – complex – and that you’re required to have the highest grade education in order to understand them.

Get this from a library. Rough sets, fuzzy sets, data mining and granular computing: 11th international conference, RSFDGrCToronto, Canada, May. Machine learning provides practical tools for analyzing data and making predictions but also powers the latest advances in artificial intelligence.

Our book provides a highly accessible introduction to the area and also caters for readers who want to delve into modern probabilistic modeling and deep learning approaches.

Contents Preface xi Foreword by Murat M. Tanik xvii Foreword by John Kocur xix Chapter 1 Introduction to Data Mining 1 - Selection from Practical Applications of Data Mining [Book]. I have read several data mining books for teaching data mining, and as a data mining researcher.

If you come from a computer science profile, the best one is in my opinion: "Introduction to Data Mining" by Tan, Steinbach and Kumar. It is a book th. Get this from a library. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing 15th International Conference, RSFDGrCTianjin, China.

The book also discusses the mining of web data, spatial data, temporal data and text data. This book can serve as a textbook for students of computer science, mathematical science and management science. It can also be an excellent handbook for researchers in the area of data mining and data : Arun K.

Pujari. In this chapter, we discuss the theory and foundational issues in data mining, describe data mining methods and algorithms, and review data mining applications. Since a major focus of this book is on rough sets and its applications to database mining.

Keywords: Rough Sets Theory, Data Mining, Complete Decision Table, Rule Discovery 1. Introduction Data mining and usage of the useful patterns that reside in the databases have become a very important research area because of the Cited by: 4 Rough Set Theory with Applications to Data Mining Obviously, IND(B) is an equivalence relation.A convenient way to present equivalence relations is through partitions.

A partition of U is a family of mutu- ally disjoint nonempty subsets of U, called blocks, such that the union of all blocks is partition induced by IND(B) will be denoted by B*.

Dissertations and Monographs.- Approximate Boolean Reasoning: Foundations and Applications in Data Mining. (source: Nielsen Book Data) Summary This book is dedicated to the monumental life, work and creative genius of Zdzislaw Pawlak, the. The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other.

It can also be an excellent handbook for researchers in the area of data mining and data warehousing. The revised edition includes a comprehensive chapter on rough set theory. The rough set theory, which is a tool of sets and relations for studying imprecision, vagueness, and uncertainty in data analysis, is a relatively new mathematical and.

Read Rough Sets and Data Mining Analysis of Imprecise Data Ebook Online.advantage of rough set theory is that it does not need any preliminary or addi-tional information about data (like prior probability in probability theory, basic probability number in Dempster-Shafer theory, and grade of membership in fuzzy set theory).

Rough Sets Let U be a nonempty set, called the universe, and let R be an equivalence rela-File Size: 36KB.