R and Data Mining: Examples and Case Studies

R and Data Mining: Examples and Case Studies

Volumn , Issue , 2012, Pages 1-234

  Zhao, Yangchang ^a  

^a Senior Data Mining Specialist   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84902586506     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/C2011-0-06686-3     Document Type: Book

References (155)

1. Han J., Kamber M. Data Mining: Concepts and Techniques 2000, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
2. Hand D.J., Mannila H., Smyth P. Principles of Data Mining (Adaptive Computation and Machine Learning) 2001, The MIT Press.
3. Witten I., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann, San Francisco, CA, USA. second ed.
4. R Development Core Team, 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-07-0.
5. Venables, W.N., Smith, D.M., R Development Core Team, 2010. An Introduction to R. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-12-7.
6. R Development Core Team, 2010b. R Language Definition. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-13-5.
7. Frank A., Asuncion A. UCI Machine Learning Repository 2010, School of Information and Computer Sciences, University of California, Irvine, http://archive.ics.uci.edu/mlurlhttp://archive.ics.uci.edu/ml.
8. Hothorn, T., Buehlmann, P., Kneib, T., Schmid, M., Hofner, B., 2012. mboost: model-based boosting. R package version 2.1-2.
9. R-core, 2012. Foreign: read data stored by Minitab, S, SAS, SPSS, Stata, Systat, dBase, R package version 0.8-49.
10. R Development Core Team, 2010a. R Data Import/Export. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-10-0.
11. Ripley, B., from 1999 to October 2002 Michael Lapsley, 2012. RODBC: ODBC database access. R package version 1.3-5.
12. Ligges U., Mächler M. Scatterplot3dan r package for visualizing multivariate data. Journal of Statistical Software 2003, 8(11):1-20.
13. Adler D., Murdoch D. rgl: 3D visualization device system (OpenGL). R package version 2012, (92):879.
14. Sarkar D. Lattice: Multivariate Data Visualization with R 2008, Springer, New York.
15. Wickham H. ggplot2: Elegant Graphics for Data Analysis 2009, Springer, New York.
16. Hothorn, T., Hornik, K., Strobl, C., Zeileis, A., 2010. Party: a laboratory for recursive partytioning. http://cran.r-project.org/web/packages/party/.
17. Therneau, T.M., Atkinson, B., Ripley, B., 2010. rpart: Recursive partitioning. R package version 3.1-46.
18. Liaw A., Wiener M. Classification and regression by randomforest. R News 2002, 2(3):18-22.
19. Zhao Y., Cao L., Zhang H., Zhang C. Data Clustering. Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends 2009, Information Science Reference, pp. 562-572. ISBN: 978-1-60566-242-8.
20. Jain A.K., Murty M.N., Flynn P.J. Data clustering: a review. ACM Computing Surveys 1999, 31(3):264-323.
21. Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K., 2012. cluster: cluster analysis basics and extensions. R package version 1.14.2.
22. Hennig, C., 2010. fpc: flexible procedures for clustering. R package version 2.0-3.
23. Ester, M., Kriegel, H.-P., Sander, J., Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp. 226-231.
24. Breunig M.M., Kriegel H.-P., Ng R.T., Sander J. LOF: identifying density-based local outliers. SIGMOD '00: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data 2000, 93-104. ACM Press, New York, NY, USA.
25. Torgo L. Data Mining with R, learning with case studies 2010, Chapman and Hall/CRC.
26. Hu Y., Murray W., Shan Y. Rlof: R parallel implementation of Local Outlier Factor(LOF). R package version 2011, 1.
27. Ester, M., Kriegel, H.-P., Sander, J., and Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD, pages 226-231.b0025.
28. Cleveland R.B., Cleveland W.S., McRae J.E., Terpenning I. Stl: a seasonal-trend decomposition procedure based on loess. Journal of Official Statistics 1990, 6(1):3-73.
29. Koufakou A., Ortiz E.G., Georgiopoulos M., Anagnostopoulos G.C., Reynolds K.M. A scalable and efficient outlier detection strategy for categorical data. Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI '07, Washington, DC, USA 2007, vol. 02:210-217. IEEE Computer Society.
30. van der Loo, M., 2010. extremevalues, an R package for outlier detection in univariate data. R package version 2.0.
31. Filzmoser P., Gschwandtner M. mvoutlier: Multivariate outlier detection based on robust methods. R package version 2012, 1(9):7.
32. Komsta, L., 2011. outliers: Tests for outliers. R package version 0.14.
33. R Development Core Team, 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-07-0.
34. The Institute of Statistical Mathematics, 2012. timsac: Time series analysis and control package. R package version 1.2.7.
35. Keogh, E.J., Pazzani, M.J. 2001. Derivative dynamic time warping. In: The First SIAM International Conference on Data Mining (SDM-2001), Chicago, IL, USA.
36. Giorgino T. Computing and visualizing dynamic timewarping alignments in R: the dtw package. Journal of Statistical Software 2009, 31(7):1-24.
37. Hothorn, T., Hornik, K., Strobl, C., Zeileis, A. 2010. Party: A laboratory for recursive partytioning. http://cran.r-project.org/web/packages/party/urlhttp://cran.r-project.org/web/packages/party/.
38. Burrus, C.S., Gopinath, R.A., Guo, H. 1998. Introduction to Wavelets and Wavelet Transforms: A Primer. Prentice-Hall, Inc.
39. Agrawal R., Faloutsos C., Swami A.N. Efficient similarity search in sequence databases. Proceedings of the Fourth International Conference of Foundations of Data Organization and Algorithms (FODO), Chicago, Illinois 1993, 69-84. Springer Verlag. D. Lomet (Ed.).
40. Aldrich, E., 2010. wavelets: A package of funtions for computing wavelet filters, wavelet transforms and multiresolution analyses. http://cran.r-project.org/web/packages/wavelets/index.html.
41. Chan, K.-p., Fu, A.W.-c., 1999. Efficient time series matching by wavelets. In: Internation Conference on Data Engineering (ICDE '99), Sydney.
42. Chan F.K., Fu A.W., Yu C. Harr wavelets for efficient similarity search of time-series: with and without time warping. IEEE Trans. on Knowledge and Data. Engineering 2003, 15(3):686-705.
43. Keogh, E.J., Pazzani, M.J. 1998. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: KDD 1998, pp. 239-243.
44. Keogh E., Chakrabarti K., Pazzani M., Mehrotra S. Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems 2000, 3(3):263-286.
45. Keogh E.J., Pazzani M.J. A simple dimensionality reduction technique for fast similarity search in large time series databases 2000, PAKDD, In, pp. 122-133.
46. Mörchen, F. 2003. Time series feature extraction for data mining using DWT and DFT. Technical report, Departement of Mathematics and Computer Science Philipps-University Marburg. DWT & DFT.
47. Rafiei, D., Mendelzon, A.O. 1998. Efficient retrieval of similar time sequences using DFT. In: Tanaka, K., Ghandeharizadeh, S. (Eds.), FODO, pp. 249-257.
48. Vlachos, M., Lin, J., Keogh, E., Gunopulos, D., 2003. A wavelet-based anytime algorithm for k-means clustering of time series. In: Workshop on Clustering High Dimensionality Data and Its Applications, at the Third SIAM International Conference on Data Mining, San Francisco, CA, USA.
49. Wu, Y.-l., Agrawal, D., Abbadi, A.E., 2000. A comparison of DFT and DWT based similarity search in time-series databases. In: Proceedings of the Ninth ACM CIKM International Conference on Informationand Knowledge Management, McLean, VA, pp. 488-495.
50. Zhao Y., Zhang S. Generalized dimension-reduction framework for recent-biased time series analysis. IEEE Transactions on Knowledge and Data Engineering 2006, 18(2):231-244.
51. Alcock R.J. and Manolopoulos Y. (1999). Time-Series Similarity Queries Employing a Feature-Based Approach. In Proceedings of the 7th Hellenic Conference on Informatics. Ioannina, Greece, August 27-29, 1999.
52. Tan P.-N., Kumar V., Srivastava J. Selecting the right interestingness measure for association patterns, In: KDD '02: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2002, ACM Press, NY, USA, New York, pp 32-41.
53. Agrawal, R., Srikant, R., 1994. Fast algorithms for mining association rules in large databases. In: Proc. of the 20th International Conference on Very Large Data Bases, Santiago, Chile, pp 487-499.
54. Zaki M.J. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering 2000, 12(3):372-390.
55. Hahsler, M., Chelluboina, S., 2012. arulesViz: Visualizing Association Rules and Frequent Itemsets. R package version 0.1-5.
56. Hahsler, M., Gruen, B., Hornik, K., 2011. arules: Mining Association Rules and Frequent Itemsets. R package version 1.0-8.
57. Hahsler M., Gruen B., Hornik K. arulesa computational environment for mining association rules and frequent item sets. Journal of Statistical Software 2005, 14(15).
58. Buchta, C., Hahsler, M., and with contributions from Daniel Diaz., 2012. arulesSequences: Mining frequent sequences. R package version 0.2-1.
59. Hahsler, M., 2012. arulesNBMiner: Mining NB-Frequent Itemsets and NB-Precise Rules. R package version 0.1-2.
60. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, ISBN 978-1-60566-404-0 2009, Information Science Reference, Hershey, PA. Y. Zhao, C. Zhang, L. Cao (Eds.).
61. Gentry, J., 2012. twitteR: R based Twitter client. R package version 0.99.19.
62. Feinerer, I., 2012. tm: Text Mining Package. R package version 0.5-7.1.
63. Fellows, I., 2012. wordcloud: Word Clouds. R package version 2.0.
64. Lang, D.T., 2012a. RCurl: General network (HTTP/FTP/...) client interface for R. R package version 1.91-1.1.
65. Lang, D.T., 2012b. XML: Tools for parsing and generating XML within R and S-Plus. R package version 3.9-4.1.
66. Wu H.C., Luk R.W.P., Wong K.F., Kwok K.L. Interpreting TF-IDF term weights as making relevance decisions. ACM Transactions on Information Systems 2008, 26(3):13:1-13:37.
67. Wickham H. ggplot2: Elegant Graphics for Data Analysis 2009, Springer, New York.
68. Han J., Kamber M. Data Mining: Concepts and Techniques 2000, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
69. Hand D.J., Mannila H., Smyth P. Principles of Data Mining (Adaptive Computation and Machine Learning) 2001, The MIT Press.
70. Witten I., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann, San Francisco, CA, USA. second ed.
71. Hennig, C., 2010. fpc: Flexible procedures for clustering. R package version 2.0-3.
72. Feinerer, I., 2010. tm.plugin.mail: Text Mining E-Mail Plug-In. R package version 0.0-4.
73. Hornik, K., Rauch, J., Buchta, C., Feinerer, I., 2012. textcat: N-Gram Based Text Categorization, R package version 0.1-1.
74. Chang, J., 2011. lda: Collapsed Gibbs sampling methods for topic models, R package version 1.3.1.
75. Grün B., Hornik K. Topicmodels: an R package for fitting topic models. Journal of Statistical Software 2011, 40(13):1-30.
76. Feinerer, I., Hornik, K., Meyer, D., 2008. Text mining infrastructure in r. J. Stat. Softw. 25 (5).
77. Csardi G., Nepusz T. The igraph software package for complex network research. InterJournal, Complex Systems 2006, 1695.
78. Butts, C.T. 2010. sna: Tools for Social Network Analysis. R package version 2.2-0.
79. Chang, J., 2011. lda: Collapsed Gibbs sampling methods for topic models. R package version 1.3.1.
80. Grün B., Hornik K. Topicmodels: an R package for fitting topic models. Journal of Statistical Software 2011, 40(13):1-30.
81. Handcock, M.S., Hunter, D.R., Butts, C.T., Goodreau, S.M., Morris, M., 2003. statnet: Software tools for the Statistical Modeling of Network Data. Seattle, WA. Version 2.0.
82. Butts, C.T., Handcock, M.S., Hunter, D.R., 2012. network: Classes for Relational Data. Irvine, CA.R package version 1.7-1.
83. Hothorn, T., Hornik, K., Strobl, C., and Zeileis, A., 2010. Party: A laboratory for recursive partytioning. http://cran.r-project.org/web/packages/party/.
84. Adler, D., Murdoch, D., 2012. rgl: 3D visualization device system (OpenGL). R package version 0.92.879.
85. Agrawal, R., Srikant, R., 1994. Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, pp. 487-499.
86. Agrawal R., Faloutsos C., Swami A.N. Efficient similarity search in sequence databases. Proceedings of the Fourth International Conference of Foundations of Data Organization and Algorithms (FODO), Chicago, Illinois 1993, 69-84. Springer Verlag. D. Lomet (Ed.).
87. Alcock R.J., Manolopoulos Y., 1999. Time-series similarity queries employing a feature-based approach. In: Proceedings of the 7th Hellenic Conference on Informatics. Ioannina, Greece, August 27-29.
88. Aldrich, E., 2010. wavelets: A package of funtions for computing wavelet filters, wavelet transforms and multiresolution analyses. http://cran.r-project.org/web/packages/wavelets/index.html.
89. Breunig M.M., Kriegel H.-P., Ng R.T., Sander J. LOF: identifying density-based local outliers. SIGMOD '00: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data 2000, 93-104. ACM Press, New York, NY, USA.
90. Buchta, C., Hahsler, M., and with contributions from Daniel Diaz, 2012. arulesSequences: mining frequent sequences. R package version 0.2-1.
91. Burrus C.S., Gopinath R.A., Guo H. Introduction to Wavelets and Wavelet Transforms: A Primer 1998, Prentice-Hall, Inc.
92. Butts, C.T., 2010. sna: tools for social network analysis. R package version 2.2-0.
93. Butts, C.T., Handcock, M.S., Hunter, D.R., 2012. network: classes for relational data, Irvine, CA. R package version 1.7-1.
94. Chan, K.-p., Fu, A.W.-c., 1999. Efficient time series matching by wavelets. In: Internation Conference on Data Engineering (ICDE '99), Sydney.
95. Chan F.K., Fu A.W., Yu C. Harr wavelets for efficient similarity search of time-series: with and without time warping. IEEE Transactions on Knowledge and Data Engineering 2003, 15(3):686-705.
96. Chang, J., 2011. lda: collapsed Gibbs sampling methods for topic models. R package version 1.3.1.
97. Cleveland R.B., Cleveland W.S., McRae J.E., Terpenning I. Stl: a seasonal-trend decomposition procedure based on loess. Journal of Official Statistics 1990, 6(1):3-73.
98. Csardi G., Nepusz T. The igraph software package for complex network research. InterJournal, Complex Systems 2006, 1695.
99. Ester, M., Kriegel, H.-P., Sander, J., Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp. 226-231.
100. Feinerer, I., 2010. tm.plugin.mail: text mining e-mail plug-in. R package version 0.0-4.
101. Feinerer, I., 2012. tm: text mining package. R package version 0.5-7.1.
102. Feinerer I., Hornik K., Meyer D. Text mining infrastructure in R. Journal of Statistical Software 2008, 25(5).
103. Fellows, I., 2012. wordcloud: word clouds. R package version 2.0.
104. Filzmoser, P., Gschwandtner, M., 2012. mvoutlier: multivariate outlier detection based on robust methods. R package version 1.9.7.
105. Frank A., Asuncion A. UCI Machine Learning Repository 2010, School of Information and Computer Sciences, University of California, Irvine, http://archive.ics.uci.edu/ml.
106. Gentry, J., 2012. twitteR: R based Twitter client. R package version 0.99.19.
107. Giorgino T. Computing and visualizing dynamic timewarping alignments in R: the dtw package. Journal of Statistical Software 2009, 31(7):1-24.
108. Grün B., Hornik K. Topicmodels: an R package for fitting topic models. Journal of Statistical Software 2011, 40(13):1-30.
109. Hahsler, M., 2012. arulesNBMiner: mining NB-frequent itemsets and NB-precise rules. R package version 0.1-2.
110. Hahsler, M., Chelluboina, S., 2012. arulesViz: visualizing association rules and frequent itemsets. R package version 0.1-5.
111. Hahsler M., Gruen B., Hornik K. arulesa computational environment for mining association rules and frequent item sets. Journal of Statistical Software 2005, 14(15).
112. Hahsler, M., Gruen, B., Hornik, K., 2011. arules: mining association rules and frequent itemsets. R package version 1.0-8.
113. Han J., Kamber M. Data Mining: Concepts and Techniques 2000, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
114. Hand D.J., Mannila H., Smyth P. Principles of Data Mining (Adaptive Computation and Machine Learning) 2001, The MIT Press.
115. Handcock, M.S., Hunter, D.R., Butts, C.T., Goodreau, S.M., Morris, M., 2003. statnet: Software Tools for the Statistical Modeling of Network Data, Seattle, WA. Version 2.0.
116. Hennig, C., 2010. fpc: flexible procedures for clustering. R package version 2.0-3.
117. Hornik, K., Rauch, J., Buchta, C., Feinerer, I., 2012. textcat: N-Gram based text categorization. R package version 0.1-1.
118. Hothorn, T., Hornik, K., Strobl, C., Zeileis, A., 2010. Party: a laboratory for recursive partytioning. http://cran.r-project.org/web/packages/party/.
119. Hothorn, T., Buehlmann, P., Kneib, T., Schmid, M., Hofner, B., 2012. mboost: model-based boosting. R package version 2.1-2.
120. Hu, Y., Murray, W., Shan, Y., 2011. Rlof: R parallel implementation of Local Outlier Factor (LOF). R package version 1.0.0.
121. Jain A.K., Murty M.N., Flynn P.J. Data clustering: a review. ACM Computing Surveys 1999, 31(3):264-323.
122. Keogh, E.J., Pazzani, M.J., 1998. An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: KDD 1998, pp. 239-243.
123. Keogh, E.J., Pazzani, M.J., 2000. A simple dimensionality reduction technique for fast similarity search in large time series databases. In: PAKDD, pp. 122-133.
124. Keogh, E.J., Pazzani, M.J., 2001. Derivative dynamic time warping. In: The First SIAM International Conference on Data Mining (SDM-2001), Chicago, IL, USA.
125. Keogh E., Chakrabarti K., Pazzani M., Mehrotra S. Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems 2000, 3(3):263-286.
126. Komsta, L., 2011. outliers: tests for outliers. R package version 0.14.
127. Koufakou A., Ortiz E.G., Georgiopoulos M., Anagnostopoulos G.C., Reynolds K.M. A scalable and efficient outlier detection strategy for categorical data. Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence, ICTAI '07, Washington, DC, USA 2007, vol. 02:210-217. IEEE Computer Society.
128. Lang, D.T., 2012a. RCurl: general network (HTTP/FTP/...) client interface for R. R package version 1.91-1.1.
129. Lang, D.T., 2012b. XML: tools for parsing and generating XML within R and S-Plus. R package version 3.9-4.1.
130. Liaw A., Wiener M. Classification and regression by randomforest. R News 2002, 2(3):18-22.
131. Ligges U., Mächler M. Scatterplot3d-an R package for visualizing multivariate data. Journal of Statistical Software 2003, 8(11):1-20.
132. Mörchen, F., 2003. Time series feature extraction for data mining using DWT and DFT. Technical Report, Department of Mathematics and Computer Science, Philipps-University Marburg.
133. Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., Hornik, K., 2012. cluster: cluster analysis basics and extensions. R package version 1.14.2.
134. R Development Core Team, 2010a. R Data Import/Export. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-10-0.
135. R Development Core Team, 2010b. R Language Definition. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-13-5.
136. R Development Core Team, 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-07-0.
137. R-core, 2012. Foreign: read data stored by Minitab, S, SAS, SPSS, Stata, Systat, dBase, R package version 0.8-49.
138. Rafiei, D., Mendelzon, A.O., 1998. Efficient retrieval of similar time sequences using DFT. In: Tanaka, K., Ghandeharizadeh, S. (Eds.), FODO, pp. 249-257.
139. Ripley, B., from 1999 to October 2002 Michael Lapsley, 2012. RODBC: ODBC database access. R package version 1.3-5.
140. Sarkar D. Lattice: Multivariate Data Visualization with R 2008, Springer, New York, ISBN: 978-0-387-75968-5.
141. Tan P.-N., Kumar V., Srivastava J. Selecting the right interestingness measure for association patterns. KDD '02: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2002, 32-41. ACM Press, New York, NY, USA.
142. The Institute of Statistical Mathematics, 2012. timsac: Time series analysis and control package. R package version 1.2.7.
143. Therneau, T.M., Atkinson, B., Ripley, B., 2010. rpart: Recursive partitioning. R package version 3.1-46.
144. Torgo L. Data Mining with R-Learning with Case Studies 2010, Chapman and Hall/CRC.
145. van der Loo, M., 2010. Extremevalues, an R package for outlier detection in univariate data. R package version 2.0.
146. Venables, W.N., Smith, D.M., R Development Core Team, 2010. An Introduction to R. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-12-7.
147. Vlachos, M., Lin, J., Keogh, E., Gunopulos, D., 2003. A wavelet-based anytime algorithm for k-means clustering of time series. In: Workshop on Clustering High Dimensionality Data and Its Applications, at the Third SIAM International Conference on Data Mining, San Francisco, CA, USA.
148. Wickham H. ggplot2: Elegant Graphics for Data Analysis 2009, Springer, New York.
149. Witten I., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann, San Francisco, CA, USA. second ed.
150. Wu, Y.-l., Agrawal, D., Abbadi, A.E., 2000. A comparison of DFT and DWT based similarity search in time-series databases. In: Proceedings of the Ninth ACM CIKM International Conference on Informationand Knowledge Management, McLean, VA, pp. 488-495.
151. Wu H.C., Luk R.W.P., Wong K.F., Kwok K.L. Interpreting TF-IDF term weights as making relevance decisions. ACM Transactions on Information Systems 2008, 26(3):13:1-13:37.
152. Zaki M.J. Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering 2000, 12(3):372-390.
153. Zhao Y., Zhang S. Generalized dimension-reduction framework for recent-biased time series analysis. IEEE Transactions on Knowledge and Data Engineering 2006, 18(2):231-244.
154. Zhao Y., Cao L., Zhang H., Zhang C. Data Clustering. Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends 2009, Information Science Reference, pp. 562-572. ISBN: 978-1-60566-242-8.
155. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction 2009, Information Science Reference, Hershey, PA, ISBN: 978-1-60566-404-0. Y. Zhao, C. Zhang, L. Cao (Eds.).