Data Mining_ Concepts, Methods and Applications in Management and Engineering Design [Yin, Kaku, Tang & Zhu 2011-01-07](1).pdf

Decision Engineering

Series Editor

Professor Rajkumar Roy

Department of Enterprise Integration School of Industrial and Manufacturing Science

Cranﬁeld University

Cranﬁeld

Bedford

MK43 0AL

Other titles published in this series

Cost Engineering in Practice

John McIlwraith

IPA – Concepts and Applications in Engineering

Jerzy Pokojski

Strategic Decision Making

Navneet Bhushan and Kanwal Rai

Product Lifecycle Management

John Stark

From Product Description to Cost: A Practical Approach

Volume 1: The Parametric Approach

Pierre Foussier

From Product Description to Cost: A Practical Approach

Volume 2: Building a Speciﬁc Model

Pierre Foussier

Decision-Making in Engineering Design

Yotaro Hatamura

Composite Systems Decisions

Mark Sh. Levin

Intelligent Decision-making Support Systems

Jatinder N.D. Gupta, Guisseppi A. Forgionne and Manuel Mora T.

Knowledge Acquisition in Practice

N.R. Milton

Global Product: Strategy, Product Lifecycle Management and the Billion Customer Question

John Stark

Enabling a Simulation Capability in the Organisation

Andrew Greasley

Network Models and Optimization

Mitsuo Gen, Runewei Cheng and Lin Lin

Management of Uncertainty

Gudela Grote

Introduction to Evolutionary Algorithms

Xinjie Yu and Mitsuo Gen

Yong Yin Ikou Kaku Jiafu Tang JianMing Zhu

Data Mining

Concepts, Methods and Applications

in Management and Engineering Design

123

Yong Yin, PhD

Yamagata University

Department of Economics

and Business Management

1-4-12, Kojirakawa-cho

Yamagata-shi, 990-8560

Japan

yin@human.kj.yamagata-u.ac.jp

Ikou Kaku, PhD

Akita Prefectural University

Department of Management Science

and Engineering

Yulihonjo, 015-0055

Japan

ikou_kaku@akita-pu.ac.jp

Jiafu Tang, PhD

Northeastern University

Department of Systems Engineering

110006 Shenyang

China

jftang@mail.neu.edu.cn

JianMing Zhu, PhD

Central University

of Finance and Economics

School of Information

Beijing

China

tyzjm65@163.com

ISBN

978-1-84996-337-4

e-ISBN

978-1-84996-338-1

DOI 10.1007/978-1-84996-338-1

Springer London Dordrecht Heidelberg New York

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Preface

Today’s business can be described by a single word: turbulence. Turbulent mar-

kets have the following characteristics: shorter product life cycles, uncertain product

types, and ﬂuctuating production volumes (sometimes mass, sometimes batch, and

sometimes very small volumes).

In order to survive and thrive in such a volatile business environment, a num-

ber of approaches have been developed to aid companies in their management

decisions and engineering designs. Among various methods, data mining is a rel-

atively new approach that has attracted a lot of attention from business man-

agers, engineers and academic researchers. Data mining has been chosen as one

of ten emerging technologies that will change the world by MIT Technology Re-

view .

Data mining is a process of discovering valuable information from observa-

tional data sets, which is an interdisciplinary ﬁeld bringing together techniques from

databases, machine learning, optimization theory, statistics, pattern recognition, and

visualization.

Data mining has been widely used in various areas such as business, medicine,

science, and engineering. Many books have been published to introduce data-mining

concepts, implementation procedures and application cases. Unfortunately, very few

publications interpret data-mining applications from both management and engi-

neering perspectives.

This book introduces data-mining applications in the areas of management and

industrial engineering. This book consists of the following: Chapters 1–6 provide

a focused introduction of data-mining methods that are used in the latter half of the

book. These chapters are not intended to be an exhaustive, scholarly treatise on data

mining. It is designed only to discuss the methods commonly used in management

and engineering design. The real gem of this book lies in Chapters 7–14, where

we introduce how to use data-mining methods to solve management and industrial

engineering design problems. The details of this book are as follows.

In Chapter 1, we introduce two simple but widely used methods: decision anal-

ysis and cluster analysis. Decision analysis is used to make decisions under an un-

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