Applications eligible for data mining
of products in a factory . Incremental processes analyze
data using preliminary views and then, narrow down the
views to important candidate issues in order to identify in-
sights in data. For example, important characteristics of
analyses requires techniques associated with analysis of
planning and management. In these contexts, data mining
techniques together with their applications to biology,
chemistry and ecology [6,7]. Some other papers apply data
mining to evaluation tasks needed in product management
and material recycling [8,9]. Applications of evaluation and
requirement patterns for marketing . Methodologies
employed are case-based reasoning, decision rule learning
diseases in evidence-based medicine have been identiﬁed
in this way . Iterations of data analysis under diversiﬁed
views are used to support idea creation to develop new
products and services . The design of such meta-level
management in system identiﬁcation are also seen [10,11].
Some recent work applied data mining to design support
in architecture and manufacturing ﬁelds [12,13]. The
journal also includes a paper on acquiring customer
With progress in information and communication tech-
nology, voluminous records of scientiﬁc, engineering and
social activities have been accumulated in computer net-
works. Data mining techniques attract much industrial
attention since they enable development of powerful tools
to extract knowledge from data [1,2]. Information retrieval
and statistical analysis are the conventional techniques
relevant to data mining, where the former is to seek speciﬁc
knowledge and facts that are targeted by users, and the
latter is to identify trends in data using probabilistic theory
and statistics. In general, information retrieval relies on
eﬃcient search, whereas statistical analysis processes
limited amounts of data, and uses sampling techniques to
reduce computational costs if necessary. In contrast, mod-
ern data mining applications involve diﬀerent analysis
objectives. The main objective is often to discover knowl-
edge of trends that are embedded in data. However, mod-
ern data mining applications do not exclude information
retrieval and statistical analyses. Data mining often uses
these techniques in combination with machine learning
methodologies to analyze data.
Data mining processes consist of tasks such as cleaning/
integration/selection/transformation of data, and presenta-
tion/evaluation of analysis results . These are interac-
tively repeated until analysis goals are achieved. The
success of data mining depends on the meta-level design
of users’ work processes. Top–down and hierarchical
decomposition of users’ analysis goals and applications of
data mining along this hierarchy are used under clear and
concrete objectives such as those governing quality control
Advanced Engineering Inform
1474-0346/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.
e for data mining
involves synthetic engineering to employ a range of tech-
niques appropriately in order to achieve objectives.
Recently, we have observed successful applications of
data mining that are based on well-designed data mining
processes in various industries. For example, candidate
customers who have high sensitivity to direct mail advertise-
ments are eﬃciently extracted from data of customers’
behavior in the ﬁeld of marketing. Knowledge extraction
to ﬁnd loyal customers for sales promotions is attempted
in applications based on point-of-sales (POS) data. The
characterization of web-site visitors is another popular
application of data mining in marketing. In ﬁnance, credit
risks of individuals and companies are successfully evalu-
ated based on their attribute records. The detection of can-
didate fraud transactions in credit card records is another
successful application. A major application of data mining
in manufacturing is quality control. For example, monitor-
ing the production yield of semi-conductor LSI and diagno-
sis of its degradation is an application that has resulted in
cost reductions. Defect detection and diagnosis of consumer
products is another application that is crucial to product
liability. However, compared with service industries, data
mining has been used in limited ﬁelds in manufacturing.
This is because the grasp of entire trends governing product
manufacture is a major task compared with the capture of
segmented characteristics in other applications.
The volumes of Advanced Engineering Informatics
(AEI) in the last ﬁve years include approximately ten
papers related to techniques and applications of data min-
ing. Papers propose novel and basic data mining
s 21 (2007) 241–242
and artiﬁcial neural networks. All of these techniques are
well-known and are supported by widely applicable tools
due to their long research history. Particularly, case-based
reasoning consequences and responsible use.
Technology (EPFL), Switzerland and T. Tomiyama, Intel-
ligent Mechanical Systems, Delft University of Techno-
logy, Netherlands for their extensive supports to complete
this Special Issue.
242 Editorial / Advanced Engineering Informatics 21 (2007) 241–242
Papers describing promising applications of novel data
mining techniques have been presented in many interna-
tional conferences and workshops on the data mining.
Conferences and journals dedicated to each application
domain also contain signiﬁcant numbers of data mining
applications. However, domain crossing publications
focusing on technical achievements in applications rather
than technical novelty are not available. Such publications
will clarify not only useful data mining techniques for
various domains but also techniques are identiﬁed for
future study of data mining. This Special Issue on applica-
tions eligible for data mining will provide such clariﬁcation.
Also, this issue is intended to promote development of data
mining applications in general.
Five papers are included in this Special Issue. The ﬁrst
paper, authored by Jan Ramon et al., presents the applica-
tion of data mining methods to predict the evolution of
patients in an intensive care unit. The second paper of
Yufei Shu presents the application to a nonlinear structural
health inference technique for nuclear power plants based
on interactive data mining. The third paper, authored by
Yu-Chiang Lia et al., addresses the privacy preserving
problem of ﬁnding optimal sanitization methods where
all restrictive itemsets are concealed while minimizing costs.
The fourth paper by Arijit Laha discusses a data driven
method of building credit scoring models. The ﬁnal paper
authored by Sung Ho Ha presents a marketing analysis
framework based on customer segmentation and a cus-
tomer model of the transition among segments. The data
mining techniques that are used in these papers share char-
acteristics with research that has been reported in this
journal over the last ﬁve years. Many of them employ rule
learning and neural network frameworks. The interpret-
ability of the outcome, the applicability and the eﬃcient
implementation are important issues for data mining appli-
cations, and these issues related to synthetic engineering
should be addressed in future studies of data mining.
I express my gratitude to I.F.C. Smith in Applied Com-
puting and Mechanics (IMAC), Swiss Federal Institute of
reasoning and decision rule learning provide interpretable
grounds for reasoning outcomes in form of past cases
and rules. These technical characteristics are keys for suc-
cessful applications of data mining since they are advanta-
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degradation assessment methods for enhanced rolling element bearing
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improving the reliability of system identiﬁcation, Advanced Engi-
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The Institute of Scientiﬁc and Industrial Research (ISIR),
Osaka University, 8-1, Mihogaoka, Ibaraki City,
Osaka 567-0047, Japan
E-mail address: washio@ar:sanken:osaka-u:ac:jp
Applications eligible for data mining
tor l of products in a factory . Incremental processes analyze data using preliminary views and then, narrow down the views to important candidate issues in order to identify…