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outlier analysis. Give examples of each data mining functionality, using a real-life
database that you are familiar with.
1.4 Present an example where data mining is crucial to the success of a business. What data
mining functionalities does this business need (e.g., think of the kinds of patterns that
could be mined)? Can such patterns be generated alternatively by data query processing
or simple statistical analysis?
1.5 Explain the difference and similarity between discrimination and classification, between
characterization and clustering, and between classification and regression.
1.6 Based on your observations, describe another possible kind of knowledge that needs to
be discovered by data mining methods but has not been listed in this chapter. Does it
require a mining methodology that is quite different from those outlined in this chapter?
1.7 Outliers are often discarded as noise. However, one person’s garbage could be another’s
treasure. For example, exceptions in credit card transactions can help us detect the
fraudulent use of credit cards. Using fraudulence detection as an example, propose two
methods that can be used to detect outliers and discuss which one is more reliable.
1.8 Describe three challenges to data mining regarding
data mining methodology and
user
interaction issues.
1.9 What are the major challenges of mining a huge amount of data (e.g., billions of tuples)
in comparison with mining a small amount of data (e.g., data set of a few hundred
tuple)?
1.10 Outline the major research challenges of data mining in one specific application domain,
such as stream/sensor data analysis, spatiotemporal data analysis, or bioinformatics.
1.10
Bibliographic Notes
The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley
[P-SF91], is an early collection of research papers on knowledge discovery from data.
The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad,
Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSS+96], is a collection of later research
results on knowledge discovery and data mining. There have been many data min-
ing books published in recent years, including The Elements of Statistical Learning
by Hastie, Tibshirani, and Friedman [HTF09]; Introduction to Data Mining by Tan,
Steinbach, and Kumar [TSK05]; Data Mining: Practical Machine Learning Tools and
Techniques with Java Implementations by Witten, Frank, and Hall [WFH11]; Predic-
tive Data Mining by Weiss and Indurkhya [WI98]; Mastering Data Mining: The Art
and Science of Customer Relationship Management by Berry and Linoff [BL99]; Prin-
ciples of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila,
and Smyth [HMS01]; Mining the Web: Discovering Knowledge from Hypertext Data by
Chakrabarti [Cha03a]; Web Data Mining: Exploring Hyperlinks, Contents, and Usage
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Chapter 1 Introduction
Data by Liu [Liu06];
Data Mining: Introductory and Advanced Topics by Dunham
[Dun03]; and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra
and Acharya [MA03].
There are also books that contain collections of papers or chapters on particular
aspects of knowledge discovery—for example, Relational Data Mining edited by Dze-
roski and Lavrac [De01]; Mining Graph Data edited by Cook and Holder [CH07]; Data
Streams: Models and Algorithms edited by Aggarwal [Agg06];
Next Generation of Data
Mining edited by Kargupta, Han, Yu, et al. [KHY
+
08]; Multimedia Data Mining: A Sys-
tematic Introduction to Concepts and Theory edited by Z. Zhang and R. Zhang [ZZ09];
Geographic Data Mining and Knowledge Discovery edited by Miller and Han [MH09];
and Link Mining: Models, Algorithms and Applications edited by Yu, Han, and Falout-
sos [YHF10]. There are many tutorial notes on data mining in major databases, data
mining, machine learning, statistics, and Web technology conferences.
KDNuggets is a regular electronic newsletter containing information relevant to
knowledge discovery and data mining, moderated by Piatetsky-Shapiro since 1991.
The Internet site KDNuggets (www.kdnuggets.com) contains a good collection of KDD-
related information.
The data mining community started its first international conference on knowledge
discovery and data mining in 1995. The conference evolved from the four inter-
national workshops on knowledge discovery in databases, held from 1989 to 1994.
ACM-SIGKDD, a Special Interest Group on Knowledge Discovery in Databases was
set up under ACM in 1998 and has been organizing the international conferences on
knowledge discovery and data mining since 1999. IEEE Computer Science Society has
organized its annual data mining conference, International Conference on Data Min-
ing (ICDM), since 2001. SIAM (Society on Industrial and Applied Mathematics) has
organized its annual data mining conference, SIAM Data Mining Conference (SDM),
since 2002. A dedicated journal, Data Mining and Knowledge Discovery, published by
Kluwers Publishers, has been available since 1997. An ACM journal, ACM Transactions
on Knowledge Discovery from Data, published its first volume in 2007.
ACM-SIGKDD also publishes a bi-annual newsletter, SIGKDD Explorations. There
are a few other international or regional conferences on data mining, such as the
European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases (ECML PKDD), the Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD), and the International Conference on Data
Warehousing and Knowledge Discovery (DaWaK).
Research in data mining has also been published in books, conferences, and jour-
nals on databases, statistics, machine learning, and data visualization. References to such
sources are listed at the end of the book.
Popular textbooks on database systems include Database Systems: The Complete Book
by Garcia-Molina, Ullman, and Widom [GMUW08]; Database Management Systems by
Ramakrishnan and Gehrke [RG03]; Database System Concepts by Silberschatz, Korth,
and Sudarshan [SKS10]; and Fundamentals of Database Systems by Elmasri and Navathe
[EN10]. For an edited collection of seminal articles on database systems, see Readings in
Database Systems by Hellerstein and Stonebraker [HS05].