数据挖掘入门指南：应对海量数据挑战

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《数据挖掘介绍》(Introduce to Data Mining 2013) 是由Pang-Ning Tan、Michael Steinbach和Vipin Kumar三位作者共同编著的一本权威著作，该书在快速发展的数据收集和存储技术背景下应运而生。随着组织机构积累了大量数据，如何从中提取有价值的信息成为了一项极具挑战性的任务。本书作为Pearson出版的作品，旨在为读者提供全面的数据挖掘基础理论和技术指导。全书分为多个章节，涵盖了数据挖掘的关键领域： 1. **第一章：绪论** - 为读者介绍了数据挖掘的基本概念，帮助理解这个领域的核心目标和重要性，以及它在实际商业和科研中的应用。 2. **第二章至第六章：高级概念与算法** - 进一步探讨了数据挖掘的几个核心主题： - **第2章：聚类分析** - 包括基本概念和算法，如层次聚类、K-means等，阐述了如何根据数据内在结构将对象分组。 - **第3章：聚类分析：附加问题和算法** - 对聚类分析的复杂性进行深入讨论，可能涉及噪声处理、肘部法则等问题及相应的解决方案。 - **第4章：关联分析** - 提供了深度理解市场篮子分析、频繁模式挖掘等方法，有助于发现数据之间的隐含关系。 - **第5章：关联分析：高级概念** - 探讨了更复杂的关联规则和关联图，如Apriori和FP-Growth算法。 - **第6章：异常检测** - 学习如何识别和处理异常值，这对于异常行为分析、欺诈检测等领域至关重要。 3. **第七章：维度缩减**（Appendix B） - 针对高维数据处理，介绍了降维技术，如主成分分析（PCA）、线性判别分析（LDA）等，帮助减少数据冗余并提高模型效率。 4. **第八章：优化**（Appendix E） - 数据挖掘过程中的优化问题，如搜索算法、参数调整和模型选择，是提升算法性能的关键。 5. **附录D：回归** - 介绍了回归分析在数据挖掘中的应用，包括线性回归、逻辑回归等预测模型。这本书不仅提供了理论知识，还关注实践应用，适合希望深入理解和运用数据挖掘技术的专业人士和研究人员。通过阅读，读者将掌握如何在海量数据中发掘出有价值的信息，为决策制定和业务增长提供强大支持。

1.6 Bibliographic Notes

The topic of data mining has inspired many textbooks. Introductory text-

books include those by Dunham [10], Han and Kamber [21], Hand et al. [23],

and Roiger and Geatz [36]. Data mining books with a stronger emphasis on

business applications include the works by Berry and Linoﬀ [2], Pyle [34], and

Parr Rud [33]. Books with an emphasis on statistical learning include those

by Cherkassky and Mulier [6], and Hastie et al. [24]. Some books with an

emphasis on machine learning or pattern recognition are those by Duda et

al. [9], Kantardzic [25], Mitchell [31], Webb [41], and Witten and Frank [42].

There are also some more specialized books: Chakrabarti [4] (web mining),

Fayyad et al. [13] (collection of early articles on data mining), Fayyad et al.

[11] (visualization), Grossman et al. [18] (science and engineering), Kargupta

and Chan [26] (distributed data mining), Wang et al. [40] (bioinformatics),

and Zaki and Ho [44] (parallel data mining).

There are several conferences related to data mining. Some of the main

conferences dedicated to this ﬁeld include the ACM SIGKDD International

Conference on Knowledge Discovery and Data Mining (KDD), the IEEE In-

ternational Conference on Data Mining (ICDM), the SIAM International Con-

ference on Data Mining (SDM), the European Conference on Principles and

Practice of Knowledge Discovery in Databases (PKDD), and the Paciﬁc-Asia

Conference on Knowledge Discovery and Data Mining (PAKDD). Data min-

ing papers can also be found in other major conferences such as the ACM

SIGMOD/PODS conference, the International Conference on Very Large Data

Bases (VLDB), the Conference on Information and Knowledge Management

(CIKM), the International Conference on Data Engineering (ICDE), the In-

ternational Conference on Machine Learning (ICML), and the National Con-

ference on Artiﬁcial Intelligence (AAAI).

Journal publications on data mining include IEEE Transactions on Knowl-

edge and Data Engineering, Data Mining and Knowledge Discovery, Knowl-

edge and Information Systems, Intelligent Data Analysis, Information Sys-

tems, and the Journal of Intelligent Information Systems.

There have been a number of general articles on data mining that deﬁne the

ﬁeld or its relationship to other ﬁelds, particularly statistics. Fayyad et al. [12]

describe data mining and how it ﬁts into the total knowledge discovery process.

Chen et al. [5] give a database perspective on data mining. Ramakrishnan

and Grama [35] provide a general discussion of data mining and present several

viewpoints. Hand [22] describes how data mining diﬀers from statistics, as does

Friedman [14]. Lambert [29] explores the use of statistics for large data sets and

provides some comments on the respective roles of data mining and statistics.

Chapter 1 Introduction

Glymour et al. [16] consider the lessons that statistics may have for data

mining. Smyth et al. [38] describe how the evolution of data mining is being

driven by new types of data and applications, such as those involving streams,

graphs, and text. Emerging applications in data mining are considered by Han

et al. [20] and Smyth [37] describes some research challenges in data mining.

A discussion of how developments in data mining research can be turned into

practical tools is given by Wu et al. [43]. Data mining standards are the

subject of a paper by Grossman et al. [17]. Bradley [3] discusses how data

mining algorithms can be scaled to large data sets.

With the emergence of new data mining applications have come new chal-

lenges that need to be addressed. For instance, concerns about privacy breaches

as a result of data mining have escalated in recent years, particularly in ap-

plication domains such as Web commerce and health care. As a result, there

is growing interest in developing data mining algorithms that maintain user

privacy. Developing techniques for mining encrypted or randomized data is

known as privacy-preserving data mining. Some general references in this

area include papers by Agrawal and Srikant [1], Clifton et al. [7] and Kargupta

et al. [27]. Vassilios et al. [39] provide a survey.

Recent years have witnessed a growing number of applications that rapidly

generate continuous streams of data. Examples of stream data include network

traﬃc, multimedia streams, and stock prices. Several issues must be considered

when mining data streams, such as the limited amount of memory available,

the need for online analysis, and the change of the data over time. Data

mining for stream data has become an important area in data mining. Some

selected publications are Domingos and Hulten [8] (classiﬁcation), Giannella

et al. [15] (association analysis), Guha et al. [19] (clustering), Kifer et al. [28]

(change detection), Papadimitriou et al. [32] (time series), and Law et al. [30]

(dimensionality reduction).

Bibliography

[1] R. Agrawal and R. Srikant. Privacy-preserving data mining. In Proc. of 2000 ACM-

SIGMOD Intl. Conf. on Management of Data, pages 439–450, Dallas, Texas, 2000.

ACM Press.

[2] M.J.A.BerryandG.Linoﬀ. Data Mining Techniques: For Marketing, Sales, and

Customer Relationship Management. Wiley Computer Publishing, 2nd edition, 2004.

[3] P. S. Bradley, J. Gehrke, R. Ramakrishnan, and R. Srikant. Scaling mining algorithms

to large databases. Communications of the ACM, 45(8):38–43, 2002.

[4] S. Chakrabarti. Mining the Web: Discovering Knowledge from Hypertext Data. Morgan

Kaufmann, San Francisco, CA, 2003.

Bibliography

[5] M.-S. Chen, J. Han, and P. S. Yu. Data Mining: An Overview from a Database

Perspective. IEEE Transactions on Knowledge abd Data Engineering, 8(6):866–883,

1996.

[6] V. Cherkassky and F. Mulier. Learning from Data: Concepts, Theory, and Methods.

Wiley Interscience, 1998.

[7] C. Clifton, M. Kantarcioglu, and J. Vaidya. Deﬁning privacy for data mining. In

National Science Foundation Workshop on Next Generation Data Mining, pages 126–

133, Baltimore, MD, November 2002.

[8] P. Domingos and G. Hulten. Mining high-speed data streams. In Proc. of the 6th Intl.

Conf. on Knowledge Discovery and Data Mining, pages 71–80, Boston, Massachusetts,

2000. ACM Press.

[9] R.O.Duda,P.E.Hart,andD.G.Stork. Pattern Classiﬁcation. John Wiley & Sons,

Inc., New York, 2nd edition, 2001.

[10] M. H. Dunham. Data Mining: Introductory and Advanced Topics. Prentice Hall, 2002.

[11] U. M. Fayyad, G. G. Grinstein, and A. Wierse, editors. Information Visualization in

Data Mining and Knowledge Discovery. Morgan Kaufmann Publishers, San Francisco,

CA, September 2001.

[12] U. M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From Data Mining to Knowledge

Discovery: An Overview. In Advances in Knowledge Discovery and Data Mining, pages

1–34. AAAI Press, 1996.

[13] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors. Advances

in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.

[14] J. H. Friedman. Data Mining and Statistics: What’s the Connection? Unpublished.

www-stat.stanford.edu/∼jhf/ftp/dm-stat.ps, 1997.

[15] C. Giannella, J. Han, J. Pei, X. Yan, and P. S. Yu. Mining Frequent Patterns in Data

Streams at Multiple Time Granularities. In H. Kargupta, A. Joshi, K. Sivakumar, and

Y. Yesha, editors, Next Generation Data Mining, pages 191–212. AAAI/MIT, 2003.

[16] C. Glymour, D. Madigan, D. Pregibon, and P. Smyth. Statistical Themes and Lessons

for Data Mining. Data Mining and Knowledge Discovery, 1(1):11–28, 1997.

[17] R. L. Grossman, M. F. Hornick, and G. Meyer. Data mining standards initiatives.

Communications of the ACM, 45(8):59–61, 2002.

[18] R. L. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, and R. Namburu, editors. Data

Mining for Scientiﬁc and Engineering Applications. Kluwer Academic Publishers, 2001.

[19] S. Guha, A. Meyerson, N. Mishra, R. Motwani, and L. O’Callaghan. Clustering Data

Streams: Theory and Practice. IEEE Transactions on Knowledge and Data Engineering,

15(3):515–528, May/June 2003.

[20] J. Han, R. B. Altman, V. Kumar, H. Mannila, and D. Pregibon. Emerging scientiﬁc

applications in data mining. Communications of the ACM, 45(8):54–58, 2002.

[21] J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann

Publishers, San Francisco, 2001.

[22] D. J. Hand. Data Mining: Statistics and More? The American Statistician, 52(2):

112–118, 1998.

[23] D. J. Hand, H. Mannila, and P. Smyth. Principles of Data Mining. MIT Press, 2001.

[24] T. Hastie, R. Tibshirani, and J. H. Friedman. The Elements of Statistical Learning:

Data Mining, Inference, Prediction. Springer, New York, 2001.

[25] M. Kantardzic. Data Mining: Concepts, Models, Methods, and Algorithms. Wiley-IEEE

Press, Piscataway, NJ, 2003.

Chapter 1 Introduction

[26] H. Kargupta and P. K. Chan, editors. Advances in Distributed and Parallel Knowledge

Discovery. AAAI Press, September 2002.

[27] H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar. On the Privacy Preserving Prop-

erties of Random Data Perturbation Techniques. In Proc. of the 2003 IEEE Intl. Conf.

on Data Mining, pages 99–106, Melbourne, Florida, December 2003. IEEE Computer

Society.

[28] D. Kifer, S. Ben-David, and J. Gehrke. Detecting Change in Data Streams. In Proc. of

the 30th VLDB Conf., pages 180–191, Toronto, Canada, 2004. Morgan Kaufmann.

[29] D. Lambert. What Use is Statistics for Massive Data? In ACM SIGMOD Workshop

on Research Issues in Data Mining and Knowledge Discovery, pages 54–62, 2000.

[30] M. H. C. Law, N. Zhang, and A. K. Jain. Nonlinear Manifold Learning for Data

Streams. In Proc. of the SIAM Intl. Conf. on Data Mining, Lake Buena Vista, Florida,

April 2004. SIAM.

[31] T. Mitchell. Machine Learning. McGraw-Hill, Boston, MA, 1997.

[32] S. Papadimitriou, A. Brockwell, and C. Faloutsos. Adaptive, unsupervised stream min-

ing. VLDB Journal, 13(3):222–239, 2004.

[33] O. Parr Rud. Data Mining Cookbook: Modeling Data for Marketing, Risk and Customer

Relationship Management. John Wiley & Sons, New York, NY, 2001.

[34] D. Pyle. Business Modeling and Data Mining. Morgan Kaufmann, San Francisco, CA,

2003.

[35] N. Ramakrishnan and A. Grama. Data Mining: From Serendipity to Science—Guest

Editors’ Introduction. IEEE Computer, 32(8):34–37, 1999.

[36] R. Roiger and M. Geatz. Data Mining: A Tutorial Based Primer. Addison-Wesley,

2002.

[37] P. Smyth. Breaking out of the Black-Box: Research Challenges in Data Mining. In

Proc. of the 2001 ACM SIGMOD Workshop on Research Issues in Data Mining and

Knowledge Discovery , 2001.

[38] P. Smyth, D. Pregibon, and C. Faloutsos. Data-driven evolution of data mining algo-

rithms. Communications of the ACM, 45(8):33–37, 2002.

[39] V. S. Verykios, E. Bertino, I. N. Fovino, L. P. Provenza, Y. Saygin, and Y. Theodoridis.

State-of-the-art in privacy preserving data mining. SIGMOD Record, 33(1):50–57, 2004.

[40] J. T. L. Wang, M. J. Zaki, H. Toivonen, and D. E. Shasha, editors. Data Mining in

Bioinformatics. Springer, September 2004.

[41] A. R. Webb. Statistical Pattern Recognition. John Wiley & Sons, 2nd edition, 2002.

[42] I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Tech-

niques with Java Implementations. Morgan Kaufmann, 1999.

[43] X. Wu, P. S. Yu, and G. Piatetsky-Shapiro. Data Mining: How Research Meets Practical

Development? Knowledge and Information Systems, 5(2):248–261, 2003.

[44] M. J. Zaki and C.-T. Ho, editors. Large-Scale Parallel Data Mining. Springer, September

2002.

1.7 Exercises

1. Discuss whether or not each of the following activities is a data mining task.

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