Abstract
With the advances of information technology and widespread diffusion of databases systems in organizations, large volumes of data are generated and collected by organizations. This dramatic expansion of data has generated an urgent need for new analysis techniques that can intelligently and automatically transform the processed data into useful information and knowledge. As a result, knowledge discovery and data mining have increased in importance and economic value. Knowledge discovery refers to the overall process of discovering useful knowledge from data, while data mining refers to the extraction of patterns from data. This chapter provides a reasonably comprehensive review of knowledge discovery and its associated data mining techniques. Based on the kinds of knowledge that can be discovered in databases, data mining techniques can be broadly structured into several categories, including classification, clustering, dependency analysis, data visualization, and text mining. Representative data mining techniques for each category are depicted in this chapter.
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References
Agrawal, R., R. Bayardo, and R. Srikant, “Athena: Mining-based Interactive Management of Text Databases,” Proceedings of the 6th International Conference on Extending Database Technology, July 1999, 365–379.
Agrawal, R., T. Imielinski, and A. Swami, “Mining Association Rules Between Sets of Items in Large Databases,” Proceedings of the ACM SIGMOD International Conference on Management of Data, Washington DC, 1993, 207–216.
Agrawal, R. and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, 1994, 487–499.
Agrawal R. and R. Srikant, “Mining Sequential Patterns,” Proceedings of the 1995 Conference on Data Engineering, Taiepi, Taiwan, 1995, 3–14.
Anand, S. S., A. E. Smith, P. W. Hamilton, J. S. Anand, J. G. Hughes, and P. H. Bartels, “An Evaluation of Intelligent Prognostic Systems for Colorectal Cancer,” Artificial Intelligence in Medicine, Vol. 15, No. 2, 1999, 193–214.
Anderberg, M. R., Cluster Analysis for Applications, New York: Academic Press, 1973.
Aoki, N., M. J. Wall, J. Demsar, B. Zupan, T. Granchi, M. A. Schreiber, J. B. Holcomb, M. Byrne, K. R. Liscum, G. Goodwin, J. R. Beck, and K. K. Mattox, “Predictive Model for Survival at the Conclusion of A Damage Control Laparotomy,” The American Journal of Surgery, Vol. 180, No. 6, December 2000, 540–545.
Apté, C., F. Damerau, and S. Weiss, “Automated Learning of Decision Rules for Text Categorization,” ACM Transactions on Information Systems, Vol. 12, No. 3, 1994, 233–251.
Arya, S., D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Wu, “An Optimal Algorithm for Approximate Nearest Neighbor Searching,” ACM-SIAM Symposium on Discrete Algorithms (SODA), Arlington, VA, 1994, 573–582.
Atlas, L., R. Cole, J. Connor, M. El-Sharkawi, R. J. Marks II, Y. Muthusamy, and E. Barnard, “Performance Comparisons between Backpropagation Networks and Classification Trees on Three Real-World Applications,” in Turetzky, D.S. (ed.), Neural Information Processing Systems (NIPS) 2, San Mateo, CA: Morgan Kaufmann, 1990, 622–629.
Azuaje, F., W. Dubitzky, P. Lopes, N. Black, K. Adamson, X. Wu, and J. A. White, “Predicting Coronary Disease Risk Based on Short-term RR Interval Measurements: A Neural Network Approach,” Artificial Intelligence in Medicine, Vol. 15, No. 3, 1999, 275–297.
Baker, L. D. and A. K. McCallum, “Distributional Clustering of Words for Text Classification,” Proceedings of the 21st International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, August 1998, 96–103.
Becker, R. A., S. G. Eick, and A. R. Wilks, “Visualizing Network Data,” IEEE Transactions on Visualization and Computer Graphics, Vol. 1, No. 1, March 1995, 16–28.
Berry, M. J. and G. Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Support, New York: John Wiley & Sons, Inc., 1997.
Berson, A. and S. J. Smith, Data Warehousing, Data Mining, and OLAP, New York: McGraw-Hill, 1997.
Berson, A., S. Smith, and K. Thearling, Building Data Mining Applications for CRM, New York: McGraw-Hill, 2000.
Beyer, K., J. Goldstein, R. Ramakrishnan, and U. Shaft, “When is ‘Nearest Neighbor’ Meaningful?” Proceedings of the 7th International Conference on Data Theory (ICDT), Jerusalem, Israel, 1999, 217–235.
Blum, A., Neural Network in C++, New York: Wiley, 1992.
Bonchi, F., F. Giannotti, C. Gozzi, G. Manco, M. Nanni, D. Pedreschi, C. Renso, and S. Ruggieri, “Web Log Data Warehousing and Mining for Intelligent Web Caching,” Data and Knowledge Engineering, Vol. 39, No. 2, 2001, 165–189.
Breiman, L., J. Friedman, R. Olshen and C. Stone, Classification and Regression Trees, Pacific Grove, CA: Wadsworth, 1984.
Brin, S., R. Motwani, J. Ullman, and S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Data,” Proceedings of the ACM SIGMOD International Conference on Management of Data, Tucson, AZ, 1997, 255–264.
Cabena, P., P. Hadjinian, R. Stadler, J. Verhees and A. Zanasi, Discovering Data Mining: From Concept to Implementation, Upper Saddle River, NJ: Prentice Hall, 1997.
Can, F. and E. A. Ozkarahan, “Concepts and Effectiveness of the CoverCoefficient-Based Clustering Methodology for Text Databases,” ACM Transactions on Database Systems, Vol. 15, No. 4, 1990, 483–517.
Carter, C. and J. Catlett, “Assessing Credit Card Applications Using Machine Learning,” IEEE Expert, Fall 1987, 71–79.
Chae, Y. M., S. H. Ho, K. W. Cho, D. H. Lee, and S. H. Ji, “Data Mining Approach to Policy Analysis in A Health Insurance Domain,” International Journal of Medical Informatics, Vol. 62, No. 2–3, 2001, 103–111.
Chen, M. S., J. Han, and P. S. Yu, “Data Mining: An Overview from a Database Perspective,” IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, 1996, 866–883.
Cheung, D., J. Han, V. Ng, and C. Y. Wong, “Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique,” Proceedings of the International Conference on Data Engineering, New Orleans, LA, 1996, 106–114.
Cheung, D., S. D. Lee, and B. Kao, “A General Incremental Technique for Maintaining Discovered Association Rules,” Proceedings of the 5th International Conference on Database Systems for Advanced Applications, Melbourne, Australia, 1997, 185–194.
Clark, P. and T. Niblett, “The CN2 Induction Algorithm,” Machine Learning, Vol. 3, No. 4, 1989, 261–283.
Cohen, W. W. and Y. Singer, “Context-sensitive Learning Methods for Text Categorization,” ACM Transactions on Information Systems, 17, 2, 1999, 141–173.
Consens, M. P. and A. O. Mendelzon, “Hy+: A Hygraph-Based Query and Visualization System,” Proceedings of ACM SIGMOD International Conference on Management of Data, Washington D.C., 1993, 511–516.
Cover, T. M. and P. E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Transactions on Information Theory, IT-13, 1, 1967, 21–27.
Davenport, T. H., D. W. DeLong, and M. C. Beers, “Successful Knowledge Management Projects,” Sloan Management Review, Winter 1998, 43–57.
Davenport, T. H. and L. Prusak, Working Knowledge: How Organizations Manager What They Know, Boston, MA: Harvard Business School Press, 1998.
Dey, D., S. Sarkar, and P. De, “Entity Matching in Heterogeneous Databases,” Proceedings of the 31st Hawaii International Conference on System Sciences, Kona, Hawaii, 1998.
Dobkin, D. and R. J. Lipton, “Multidimensional Search Problems,” SIAM Journal of Computing, 5, 2, 1976, 181–186.
Dumais, S., J. Platt, D. Heckerman, and M. Sahami, “Inductive Learning Algorithms and Representations for Text Categorization,” Proceedings of the 1998 ACM 7th International Conference on Information and Knowledge Management (CIKM ′98), Washington D.C., November 1998, 148–155.
El-Hamdouchi, A. and P. Willett, “Hierarchical Document Clustering Using Ward’s Method,” Proceedings of ACM Conference on Research and Development in Information Retrieval, Pisa, Italy, September 1986, 149–156.
Esposito, F., D. Malerba, and G. Semeraro, “A Comparative Analysis of Methods for Pruning Decision Trees,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 5, 1997, 476–491.
Estivill-Castro, V. and A. T. Murray, “Spatial Clustering for Data Mining with Generic Algorithms,” Technical Report FIT-TR-97–10, Queensland University of Technology, Faculty of Information Management, September 1997.
Ezawa, K. J. and S. W. Norton, “Constructing Bayesian Networks to Predict Uncollectible Telecommunications Accounts,” IEEE Expert, Vol. 11, No. 5, 1996, 45–51.
Feng, L., T. Dillon, and J. Liu, “Inter-transactional Association Rules for Multidimensional Contexts for Prediction and Their Application to Studying Meteorological Data,” Data and Knowledge Engineering, Vol. 37, No. 1, 2001, 85115.
Frawley, W., G. Piatetsky-Shapiro, and C. J. Matheus. “Knowledge Discovery in Databases: An Overview,” in Piatesky-Shapiro, G. and Frawley, W.J. (eds.), Knowledge Discovery in Databases, Cambridge, MA: AAAI/MIT Press, 1991, 1–30.
Fu, L., “Knowledge Discovery Based on Neural Networks,” Communications of the ACM, 42, 11, 1999, 47–50.
Fu, L. and E. H. Shortliffe. “The Application of Certainty Factors to Neural Computing for Rule Discovery,” IEEE Transactions on Neural Networks, 11, 3, 2000, 647–657.
Gerritsen, R., “Assessing Loan Risks: A Data Mining Case Study,” IT Professional, 1, 6, 1999, 16–21.
Han, J. and Y. Fu, “Mining Multiple-Level Association Rules in Large Databases,” IEEE Transactions on Knowledge and Data Engineering, 11, 5, 1999, 798–805.
Heckerman, D., “Bayesian Networks for Data Mining,” Data Mining and Knowledge Discovery, 1, 1, 1997, 79–119.
Hui, S. C. and G. Jha, “Data Mining for Customer Service Support,” Information and Management, 38, 1, 2000, 1–13.
Iwayama, M. and T. Tokunaga, “Cluster-based Text Categorization: A Comparison of Category Search Strategies,” Proceedings of 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, WA, July 1995, 273–281.
John, G. H., P. Miller, and R. Kerber, “Stock Selection Using Rule Induction,” IEEE Expert, 11, 5, 1996, 52–58.
Kappert, C. B. and S. W. F. Omta, “Neural Networks and Business Modeling—An Application of Neural Modeling Techniques to Prospect Profiling in the Telecommunications Industry,” Proceedings of the 30th Hawaii International Conference on System Sciences, Maui, Hawaii, 1997, 465–473.
Kass, G. V., “An Exploratory Technique for Investigating Large Quantities of Categorical Data,” Applied Statistics, 29, 1980, 119–127.
Kaufman, L. and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, New York: John Wiley & Sons, Inc., 1990.
Keim, D. A. and H. Kriegel, “Visualization Techniques for Mining Large Databases: A Comparison,” IEEE Transactions on Knowledge and Data Engineering, 8, 6, 1996, 923–927.
Klemettinen, M., H. Mannila, and H. Toivonen, “Interactive Exploration of Interesting Findings in the Telecommunication Network Alarm Sequence Analyzer (TASA),” Information and Software Technology, 41, 9, 1999, 557–567.
Kim, H. and S. Lee, “A Semi-Supervised Document Clustering Technique for Information Organization,” Proceedings of the 9th International Conference on Information and Knowledge Management (CIKM), McLean, VA, November 2000, 30–37.
Kim, S. H. and H. J. Noh, “Predictability of Interest Rates Using Data Mining Tools: A Comparative Analysis of Korea and the US,” Expert Systems with Applications, 13, 2, 1997, 85–95.
Kohonen, T., Self-Organization and Associative Memory, Berlin: Springer, 1989.
Kohonen, T., Self-Organizing Maps, Berlin: Springer, 1995.
Kukar, M., I. Kononenko, C. Groselj, K. Kralj, and J. Fettich, “Analysing and Improving the Diagnosis of Ischaemic Heart Disease with Machine Learning,” Artificial Intelligence in Medicine, 16, 1, 1999, 25–50.
Lam, W. and C. Y. Ho, “Using A Generalized Instance set for Automatic Text Categorization,” Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melboume, Australia, August 1998, 81–89.
Larkey, L. and W. Croft, “Combining Classifiers in Text Categorization,” Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Zurich, Switzerland, August 1996, 289–297.
Larsen, B. and C. Aone, “Fast and Effective Text Mining Using Linear-time Document Clustering,” Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, August 1999, 16–22.
LeBlanc, J., M. O. Ward, and N. Wittels, “Exploring N-Dimensional Databases,” Proceedings of Visualization, San Francisco, CA, 1990, 230–237.
Lee, C. H., Y. H. Kim, and P. K. Rhee, “Web Personalization Expert with Combining Collaborative Filtering and Association Rule Mining Technique,” Expert Systems with Applications, 21, 3, 2001, 131–137.
Lee, H. Y. and H. L. Ong, “Visualization Support for Data Mining,” IEEE Expert, 11, 5, 1996, 69–75.
Leu, S. S., C. N. Chen, and S. L. Chang, “Data Mining for Tunnel Support Stability: Neural Network Approach,” Automation in Construction, 10, 4, 2001, 429–441.
Leung, M. T., A. S. Chen, and H. Daouk, “Forecasting Exchange Rates Using General Regression Neural Networks,” Computers and Operations Research, 27, 11–12, 2000, 1093–1110.
Lewis, D. and M. Ringuette, “A Comparison of Two Learning Algorithms for Text Categorization,” Proceedings of Symposium on Document Analysis and Information Retrieval, 1994.
Lin, F., S. Chou, S. Pan, and Y. Chen, “Mining Time Dependency Patterns in Clinical Pathways,” International Journal of Medical Informatics, 62, 1, 2001, 11–25.
Lin, F. Y. and S. McClean, “A Data Mining Approach to the Prediction of Corporate Failure,” Knowledge-Based Systems, 14, 3–4, 2001, 189–195.
Luchetta, A., S. Manetti, and F. Francini, “Forecast: A Neural System for Diagnosis and Control of Highway Surfaces,” IEEE Intelligent Systems, 13, 3, 1998, 20–26.
Mannila, H., H. Toivonen, and A. I. Verkamo, “Discovering Frequent Episodes in Sequences,” Proceedings of First International Conference on Knowledge Discovery and Data Mining (KDD’95), Montreal, Canada, August 1995, 210–215.
Mannila, H. and H. Toivonen, “Discovering Generalized Episodes Using Minimal Occurrences,” Proceedings of Second International Conference on KnowledgeDiscovery and Data Mining, Portland, Oregon, August 2–4, 1996.
Marble, R. P. and J. C. Healy, “A Neural Network Approach to the Diagnosis of Morbidity Outcomes in Trauma Care,” Artificial Intelligence in Medicine, 15, 3, 1999, 299–307.
McCallum, A. K. and K. Nigam, “A Comparison of Event Models for Naïve Bayes Text Classification,” Proceedings of AAAI-98 Workshop on Learning for Text Categorization, 1998.
Mingers, J., “An Empirical Comparison of Selection Measures for Decision-Tree Induction,” Machine Learning, 3, 1989a, 319–341.
Mingers, J., “An Empirical Comparison of Pruning Methods for Decision Tree Induction,” Machine Learning, 4, 2, 1989b, 227–243.
Murthy, S. K., S. Kasif, and S. Salzberg, “A System for Induction of Oblique Decision Trees,” Journal of Artificial Intelligence Research, 2, 1994, 1–32.
Ng, H. T., W. B. Goh, and K. L. Low, “Feature Selection, Perceptron Learning, and A Usability Case Study for Text Categorization,” Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ′97), Philadelphia, PA, July 1997, 67–73.
Ng, R. and J. Han, “Efficient and Effective Clustering Methods for Spatial Data Mining,” Proceedings of International Conference on Very Large Data Bases, Santiago, Chile, Sept. 1994, 144–155.
Niblett, T. and I. Bratko, “Learning Decision Rules in Noisy Domains,” Research and Development in Expert Systems III: Proceedings of the 6th Technical Conference of the British Computer Society Specialist Group on Expert Systems, Brignton, December 1986, 25–34.
Pickett, R. M. and G. G. Grinstein, “Iconographics Displays for Visualizing Multidimensional Data,” Proceedings of IEEE Conference on Systems, Man and Cybernetics, 1988, 514–519.
Quinlan, J. R., “Induction of Decision Trees,” Machine Learning , 1, 1, 1986, 81106.
Quinlan, J. R., C4.5: Programs for Machine Learning, San Mateo, C A: Morgan Kaufmann, 1993.
Ramaswamy, S., S. Mahajan, and A. Silberschatz, “On the Discovery of Interesting Patterns in Association Rules,” Proceedings of the 24th International Conference on Very Large Data Bases, 1998.
Ronco, A. L., “Use of Artificial Neural Networks in Modeling Associations of Discriminant Factors: Towards An Intelligent Selective Breast Cancer Screening,” Artificial Intelligence in Medicine, 16, 3, 1999, 299–309.
Roussinov, D. and H. Chen, “Document Clustering for Electronic Meetings: An Experimental Comparison of Two Techniques,” Decision Support Systems, 27, 1–2, 1999, 67–79.
Rumelhart, D. E., G. E. Hinton, and R. J. Williams, “Learning Internal Representations by Error Propagation,” in Rumelhart, D.E. and McClelland J.L. (eds.), Parallel Distributed Processing: Explorations in the Microstructures of Cognition, Vol. 1, Cambridge, MA: MIT Press, 1986, 318–362.
Shaw, M. J., C. Subramaniam, G. W. Tan, and M. E. Welge, “Knowledge Management and Data Mining for Marketing,” Decision Support Systems, 31, 1, 2001, 127–137.
Song, H. S., J. K. Kim, and S. H. Kim, “Mining the Change of Customer Behavior in An Internet Shopping Mall,” Expert Systems with Applications, 21, 3, 2001, 157–168.
Srikant, R. and R. Agrawal, “Mining Generalized Association Rules,” Future Generation Computer Systems, 13, 2–3, 1997, 161–180.
Srikant, R. and R. Agrawal, “Mining Sequential Patterns: Generalizations and Performance Improvements,” Proceedings of the 5th International Conference on Extending Database Technology (EDBT), Avignon, France, March 1996, 3–17.
Tickle, A. B., R. Andrews, M. Golea, and J. Diederich, “The Truth Will Come to Light: Directions and Challenges in Extracting the Knowledge Embedded Within Trained Artificial Neural Networks,” IEEE Transactions on Neural Networks, 9, 6, 1998, 1057–1068.
Toivonen, H., “Sampling Large Databases for Association Rules,” Proceedings of the 22nd International Conference on Very Large Data Bases, Bombay, India, 1996, 134–145.
Tufte, E. R., The Visual Display of Quantitative Information, Cheshire, CT: Graphics Press, 1983.
Voorhees, E. M., “Implementing Agglomerative Hierarchical Clustering Algorithms for Use in Document Retrieval,” Information Processing and Management, 22, 1986, 465–476.
Walczak, S. and J. E. Scharf, “Reducing Surgical Patient Costs Through Use of An Artificial Neural Network to Predict Transfusion Requirements,” Decision Support Systems, 30, 2, 2000, 125–138.
Walley, W. J. and M. A. O’Connor, “Unsupervised Pattern Recognition for the Interpretation of Ecological Data,” Ecological Modelling, 146, 1–3, 2001, 219–230.
Wei, C., Y. H. Lee and C. M. Hsu, “Empirical Comparison of Fast Clustering Algorithms for Large Data Sets,” Proceedings of 33rd Hawaii International Conference on System Sciences, Maui, Hawaii, January 2000a.
Wei, C., S. Y. Hwang, and W. S. Yang, “Mining Frequent Temporal Patterns in Process Databases,” Proceedings of 10th Workshop on Information Technologies and Systems (WITS 2000), Brisbane, Australia, December 2000b, 175–180.
Wei, C. and Y. H. Lee, “Event Detection for Supporting Environmental Scanning: An Information Extraction-based Approach,” Proceedings of 5th Pacific Asia Conference on Information Systems (PACIS), Seoul, Korea, June 2001.
Wei, J., S. Bressan, and B. C. Ooi, “Mining Term Association Rules for Automatic Global Query Expansion: Methodology and Preliminary Results,” Proceedings of the First International Conference on Web Information Systems Engineering, 2000c, 366–373.
Weiss, S. M., C. Apte, F. J. Damerau, D. E. Johnson, F. J. Oles, T. Goetz, and T. Hampp, “Maximizing Text-Mining Performance,” IEEE Intelligent Systems, 14, 4, 1999, 63–69.
West, D., “Neural Network Credit Scoring Models,” Computers and Operations Research, 27, 11–12, 2000, 1131–1152.
Wiener, W., J. O. Pedersen, and A. S. Weigend, “A Neural Network Approach to Topic Spotting,” Proceedings of the 4th Annual Symposium on Document Analysis and Information Retrieval (SDAIR ′95), Las Vegas, NV, 1995, 317332.
Wright, W., “Business Visualization Applications,” IEEE Computer Graphics and Applications, 17, 4, 1997, 66–70.
Xu, J. and W. B. Croft, “Query Expansion Using Local and Global Document Analysis,” Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1996, 4–11.
Yang, Y., “Expert Network: Effective and Efficient Learning from Human Decisions in Text Categorization and Retrieval,” Proceedings of the 17th International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, July 1994, 13–22.
Yang, Y., J. G. Carbonell, R. D. Brown, T. Pierce, B. T. Archibald, and X. Liu, “Learning Approaches for Detecting and Tracking News Events,” IEEE Intelligent Systems, 14, 4, 1999, 32–43.
Yang, Y. and C. G. Chute, “An Example-Based Mapping Method for Text Categorization and Retrieval,” ACM Transactions on Information Systems, 12, 3, 1994, 252–277.
Yang, Y. and X. Liu, “A Re-examination of Text Categorization Methods,” Proceedings of 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, August 1999, 42–49.
Yang, Y., T. Pierce, and J. G. Carbonell, “A Study on Retrospective and On-line Event Detection,” Proceedings of 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1998, 28–36.
Zaki, M., S. Parthasarathy, W. Li, and M. Ogihara, “Evaluation of Sampling for Data Mining of Association Rules,” Proceedings of the 7th Workshop on Research Issues in Data Engineering, Birmingham, UK, 1997, 42–50.
Zaki, M., S. Parthasarathy, M. Ogihara, and W. Li, “New Algorithms for Fast Discovery of Association Rules,” Technical Report 651, Computer Science Department, University of Rochester, 1998.
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Wei, CP., Piramuthu, S., Shaw, M.J. (2003). Knowledge Discovery and Data Mining. In: Holsapple, C.W. (eds) Handbook on Knowledge Management. International Handbooks on Information Systems, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24748-7_9
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