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Tuesday, July 21, 2020 | History

8 edition of Perception-based Data Mining and Decision Making in Economics and Finance (Studies in Computational Intelligence) found in the catalog.

Perception-based Data Mining and Decision Making in Economics and Finance (Studies in Computational Intelligence)

  • 84 Want to read
  • 18 Currently reading

Published by Springer .
Written in English

    Subjects:
  • Applications of Computing,
  • Computers,
  • Applied,
  • Mathematics,
  • Computer Books: General,
  • Artificial Intelligence - General,
  • Decision Making in Economics,
  • Decision Making in Finance,
  • Mathematics / Applied,
  • Data mining,
  • Data processing,
  • Decision making,
  • Economics,
  • Finance

  • Edition Notes

    ContributionsIldar Batyrshin (Editor), Janusz Kacprzyk (Editor), Leonid Sheremetov (Editor), Lotfi A. Zadeh (Editor)
    The Physical Object
    FormatHardcover
    Number of Pages346
    ID Numbers
    Open LibraryOL9056684M
    ISBN 103540362444
    ISBN 109783540362449

    Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems Each chapter covers a new data mining technique, and then shows readers how to apply the technique for 4/5(5).   Data mining in relation to enterprise resource planning is the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making (Monk & Wagner, ). Today, data mining technology integrated measurement of different kinds of is moving into focus to measure and hedging risk.

    I. Olkin, A.R. Sampson, in International Encyclopedia of the Social & Behavioral Sciences, Data Mining. Data mining refers to a set of approaches and techniques that permit ‘nuggets’ of valuable information to be extracted from vast and loosely structured multiple data bases. For example, a consumer products manufacturer might use data mining to better understand the .   Book Description. Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R explains and demonstrates, via the accompanying open-source software, how advanced analytical tools can address various business problems. It also gives insight into some of the challenges faced when deploying these tools.

    Another useful subdivision of economic data is into micro-data and macro-data. Micro-data are collected on individual decision making units, such as individuals, households and rms. Macro-data results from aggregating over individuals, households or rms at the local or national level. Lots of data on economic activity is collected on a routine. Enforcing standards through tighter policies and controls will not improve finance analytics usage or usefulness. Instead, as a part of finance transformation efforts, business managers and finance staff should collaboratively define, develop and apply finance analytics. Decision teams with clear data governance roles and responsibilities can brainstorm problem economics, co .


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Perception-based Data Mining and Decision Making in Economics and Finance (Studies in Computational Intelligence) Download PDF EPUB FB2

The book starts with a coverage of data mining tools and techniques that may be of use and significance for economic and financial analyses and applications. Notably, fuzzy and natural language based approaches and solutions for a more human consistent dealing with decision support, time series analysis, forecasting, clustering, etc.

are discussed. Finally, we discuss the role of perception-based time series data mining and computing with words and perceptions in construction of intelligent systems that use expert knowledge and decision. Buy Perception-Based Data Mining and Decision Making in Economics and Finance by Ildar Batyrshin (Editor), Leonid Sheremetov (Editor), Lofti A.

Zadeh (Editor) online at Alibris. We have new and used copies available, in 2 editions - starting at $ Shop now. Get this from a library. Perception-based data mining and decision making in economics and finance.

[I Z Batyrshin;]. Get this from a library. Perception-based data mining and decision making in economics and finance. [I Z Batyrshin;] -- The primary goal of this book is to present to the scientific and management communities a selection of applications using recent Soft Computing (SC) and Computing with Words and Perceptions (CWP).

Perception-based data mining and decision making in economics and finance. Save on Perception-based Data Mining and Decision Making in Economics and Finance by. Shop your textbooks from Jekkle today.

The primary goal of this book is to present to the scientific and management communities a selection of applications using more recent Soft Computing (SC) and Computing with Words and Perceptions (CWP.

from book Perception-based Data Mining and Decision Making in Economics and Finance (pp) Perception Based Patterns in Time Series Data Mining Chapter. Perception-based Data Mining and Decision Making in Economics and Finance. Studies in Computational Intellige SpringerISBN view. Cite this chapter as: Batyrshin I., Sheremetov L., Herrera-Avelar R.

() Perception Based Patterns in Time Series Data Mining. In: Batyrshin I., Kacprzyk J., Sheremetov L., Zadeh L.A. (eds) Perception-based Data Mining and Decision Making in Economics and Finance. Download Perception Based Data Mining And Decision Making In Economics And Finance full book in PDF, EPUB, and Mobi Format, get it for read on your Kindle device, PC, phones or tablets.

Perception Based Data Mining And Decision Making In Economics And Finance. Find many great new & used options and get the best deals for Studies in Computational Intelligence Ser.: Perception-Based Data Mining and Decision Making in Economics and Finance (, Mixed Media) at the best online prices at eBay.

Free shipping for many products. Big Data, Mining, and Analytics: Components of Strategic Decision Making ties together big data, data mining, and analytics to explain how readers can leverage them to extract valuable insights from their data.

Facilitating a clear understanding of big data, it supplies authoritative insights from expert contributors into leveraging data. Basically, Data Mining (DM) and Operations Research (OR) are two paradigms independent of each other.

OR aims at optimal solutions of decision problems with respect to a given goal. DM is concerned with secondary analysis of large amounts of data (Hand et al., ). However, there are some commonalities. Rational decision making Decision making is often presented as a rational process, in which individuals make decisions by collecting, integrating and analysing data in a coldly rational, mechanistic way.

However, research has long shown that this is not how people make decisions. Decision making is a dynamic, contextual and personal/group. “The book is an unstructured data mining quest, which takes the reader through different features of unstructured data mining while unfolding the practical facets of Big Data.

It emphasizes more on machine learning and mining methods required for processing and decision-making. This volume presents the recent advances of the use of computation intelligence in financial decision making. The book covers all the major areas of computational intelligence and a wide range of problems in finance, such as portfolio optimization, credit risk analysis, asset valuation, financial forecasting, and trading.

Operational Research is an important scientific discipline with many new theoretical developments and practical applications. This issue of Lecture Notes in Management Science (LNMS), Vol gathers the abstracts of contributions presented at the series of International Conferences on Applied Operational Research (ICAOR) from to   Why Economics Needs Data Mining Dec 2, Cosma Shalizi urges economists to stop doing what they are doing: Fitting large complex models to a small set of highly correlated time series data.

In this book we are interested in knowledge expressed in some language (formal, semi-formal) as a kind of model that can be used to support the decision making process.

The book tackles the notion of knowledge (in the domain of medicine) from two different points of view: data mining and knowledge management. To identify the decision parameters that affect the feasibility analysis, data mining techniques are applied to analyse the Go/No Go decision‐making process in infrastructure projects.

The data mining analysis uses PFS data obtained from large‐scale infrastructure projects in Korea. Classification models found 57 hidden rules in the PFS.Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other.

As a result, the book more clearly defines the principles of business analytics for those who want to. Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other.

As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work.