Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) (Studies in Computational Intelligence)

Cover of: Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) (Studies in Computational Intelligence) |

Published by Springer .

Written in English

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Subjects:

  • Artificial intelligence,
  • Computing and Information Technology,
  • Engineering: general,
  • Computers,
  • Applied,
  • Mathematics,
  • Computer Books: General,
  • Engineering - General,
  • Congresses,
  • Artificial Intelligence - General,
  • Mathematics / Applied,
  • Data mining

Edition Notes

Book details

ContributionsTsau Young Lin (Editor), Setsuo Ohsuga (Editor), Churn-Jung Liau (Editor), Xiaohua Hu (Editor)
The Physical Object
FormatHardcover
Number of Pages376
ID Numbers
Open LibraryOL9055831M
ISBN 103540283153
ISBN 109783540283157

Download Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) (Studies in Computational Intelligence)

Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving.

Researchers, on the. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining.

NOVEL APPLICATIONS --Research issues in web structural delta mining / Qiankun Zhao [and others] --Workflow reduction for reachable-path rediscovery in workflow ming / Kwang-Hoon Kim, Clarence A. Ellis --Principal component-based anomaly detection scheme / Mei-Ling Shyu [and others] --Making better sense of the demographic data value in the data.

Get this from a library. Foundations and novel approaches in data mining. [Tsau Y Lin;] -- Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. Currently, application oriented engineers are only concerned with. Foundations and Novel Approaches in Data Mining (Studies in Computational Intelligence) 作者: Lin, T.

y.; Lin, Tsau Young; Ohsuga, Setsuo 出版社: Springer 出版年: 页数: 定价: USD 装帧: Hardcover ISBN: Foundations of Data Mining A Position Paper Dr. Bhavani Thuraisingham The MITRE Corporation (at present with the National Science Foundation) Data Mining is the process of posing queries to large amounts of data sources and extracting patterns and trends using statistical and machine learning techniques.

It integrates various technologies. Foundations of Data Mining and Knowledge Discovery contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science.

The book gives both theoretical and practical knowledge of all data mining topics. It also contains many integrated examples and figures. Every important topic is presented into two chapters, beginning with basic concepts that provide the necessary background for learning each data mining technique, then it covers more complex concepts and algorithms.

Data mining is highly effective, so long as it draws upon one or more of these techniques: 1. Tracking patterns. One of the most basic techniques in data mining is learning to recognize patterns in your data sets.

This is usually a recognition of some aberration in your data happening at regular intervals, or an ebb and flow of a certain. Foundations and Novel Approaches in Data Mining. Foundations and Novel E., Wasilewska2 A. Data Mining as Generalization: A Formal Model.

In: Young Lin T., Ohsuga S., Liau CJ., Hu X. (eds) Foundations and Novel Approaches in Data Mining. Studies in Computational Intelligence, vol 9. Buy this book on publisher's site; Reprints and. Ng and J. Han, Efficient and effective clustering method for spatial data mining, in Proc. Int. Conf.

Very Large Data Bases (VLDB’94), pages –, Santiago, Chile, Sept. Nigam, and R. Ghani, Analyzing the effectiveness and applicability of co-training, Proceedings of the Ninth International Conference on Information and. Books shelved as data-mining: Data Mining: Practical Machine Learning Tools and Techniques by Ian H.

Witten, Data Mining: Concepts and Techniques by Jiaw. Data mining algorithms, technologies, and software tools, with emphasis on advanced algorithms and software that are currently used in the industry or represent promising approaches; In one concentrated reference, Pharmaceutical Data Mining reveals the role and possibilities of these sophisticated techniques in contemporary drug discovery and.

The most commonly accepted definition of “data mining” is the discovery of “models” for data. A “model,” however, can be one of several things. We mention below the most important directions in modeling.

Statistical Modeling Statisticians were the first to use the term “data mining.” Originally, “data mining” or. "This book by Mohammed Zaki and Wagner Meira Jr is a great option for teaching a course in data mining or data science. It covers both fundamental and advanced data mining topics, explains the mathematical foundations and the algorithms of data science, includes exercises for each chapter, and provides data, slides and other supplementary material on the companion website."Reviews:   Avoiding False Discoveries: A completely new addition in the second edition is a chapter on how to avoid false discoveries and produce valid results, which is novel among other contemporary textbooks on data mining.

It supplements the discussions in the other chapters with a discussion of the statistical concepts (statistical significance, p-values, false discovery rate, permutation testing.

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New book by Mohammed Zaki and Wagner Meira Jr is a great option for teaching a course in data mining or data science. It covers both fundamental and advanced data mining topics, emphasizing the mathematical foundations and the algorithms, includes exercises for each chapter, and provides data, slides and other supplementary material on the companion website.

Foundations and Trends in Information Retrieval Vol. 2, No () 1– c Bo Pang and Lillian Lee. This is a pre-publication version; there are formatting and potentially small wording differences from the final version. DOI: xxxxxx Opinion mining and sentiment analysis Bo Pang1 and Lillian Lee2 1 Yahoo.

Research, First Ave. Free data mining books. An Introduction to Statistical Learning: with Applications in R Overview of statistical learning based on large datasets of information.

The exploratory techniques of the data are discussed using the R programming language. Modeling With Data This book focus some processes to solve analytical problems applied to data. This volume examines the application of swarm intelligence in data mining, addressing the issues of swarm intelligence and data mining using novel intelligent approaches.

The book comprises 11 chapters including an introduction reviewing fundamental definitions and important research challenges. Provides the foundations and principles needed for addressing the various challenges of developing smart cities Smart cities are emerging as a priority for research and development across the world.

They open up significant opportunities in several areas, such as economic growth, health, wellness, energy efficiency, and transportation, to promote the sustainable development of cities. Data Mining Study Materials, Important Questions List, Data Mining Syllabus, Data Mining Lecture Notes can be download in Pdf format.

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Foundations and Advances in Data Mining book. Read reviews from world’s largest community for readers. With the growing use of information technology and 4/5(2). Data Mining for Education Ryan S.J.d. Baker, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA Introduction Data mining, also called Knowledge Discovery in Databases (KDD), is the field of discovering novel and potentially useful information from large amounts of data.

Data mining has been. Building on the statistical foundations and underpinnings of data mining introduced in Data Mining I, this course covers advanced topics on data mining; mining association rules from large-scale data warehouse, hierarchical clustering, mining patterns from temporal data, semi-supervised learning, active learning and boosting.

Description Discover Novel and Insightful Knowledge from Data Represented as a Graph: Practical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data.

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Antonelli M, Ducange P, Marcelloni F and Segatori A () A novel associative classification model based on a fuzzy frequent pattern mining algorithm, Expert Systems with Applications: An International Journal,(), Online publication date: 1-Mar –Seminal book is Exploratory Data Analysis by Tukey –A nice online introduction can be found in Chapter 1 of the NIST Engineering Statistics Handbook –In data mining, clustering and anomaly detection are major areas of interest, and not thought of as just exploratory.

International Journal of Business Intelligence and Data Mining. IJBIDM provides a forum for state-of-the-art developments and research as well as current innovative activities in business intelligence, data analysis and mining. Intelligent data analysis provides powerful and effective tools for problem solving in a variety of business modelling.

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.

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An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks.

It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories.

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Download for offline reading, highlight, bookmark or take notes while you read Imbalanced Learning: Foundations, Algorithms, and Applications. Introduction to Data Mining Techniques.

In this Topic, we are going to Learn about the Data mining Techniques, As the advancement in the field of Information technology has to lead to a large number of databases in various areas.

As a result, there is a need to store and manipulate important data which can be used later for decision making and improving the activities of the business. Data mining is a subfield of computer science which blends many techniques from statistics, data science, database theory and machine learning.

Here are the major milestones and “firsts” in the history of data mining plus how it’s evolved and blended with data science and big data. The increasing volume of data in modern business and science calls for more complex and sophisticated tools.

Although advances in data mining technology have made extensive data collection much easier, it’s still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge.This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.

The book consists of three sections.

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