Improving classification models with taxonomy information

Data and Knowledge Engineering

Volumn 86, Issue , 2013, Pages 85-101

  Cagliero, Luca ^a   Garza, Paolo ^a  

^a POLITECNICO DI TORINO   (Italy)

Author keywords

Classification; Data mining; Generalized association rules; Taxonomies

Indexed keywords

ASSOCIATIVE CLASSIFIERS; CLASSIFICATION METHODS; CLASSIFICATION MODELS; DATA MINING AND KNOWLEDGE DISCOVERY; DATA MINING PROBLEMS; GENERALIZED ASSOCIATION RULES; PRE-PROCESSING STEP; RESEARCH COMMUNITIES;

CLASSIFICATION (OF INFORMATION); DATA MINING; SEMANTICS;

TAXONOMIES;

EID: 84878112168     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2013.01.005     Document Type: Article

References (48)

1. P.-N. Tan, M. Steinbach, and V. Kumar Introduction to Data Mining 2005 Addison Wesley
2. P.-N. Tan, V. Kumar, and J. Srivastava Selecting the right interestingness measure for association patterns KDD'02 2002 32 41
3. D.D. Lewis Naive (bayes) at forty: the independence assumption in information retrieval ECML'98 1998 4 15
4. J. Quinlan C4.5: Programs for Machine Learning 1993 The Morgan Kaufmann
5. T. Joachims Text categorization with support vector machines: Learning with many relevant features ECML'98 1998 137 142
6. B. Liu, W. Hsu, and Y. Ma Integrating classification and association rule mining KDD'98 1998 80 86
7. J. Li, J. Liu, M. Baig, and R.C.-W. Wong Information based data anonymization for classification utility Data & Knowledge Engineering 70 12 2011 1030 1045
8. S. Mukherjee, M. Banerjee, Z. Chen, and A. Gangopadhyay A privacy preserving technique for distance-based classification with worst case privacy guarantees Data & Knowledge Engineering 66 2 2008 264 288
9. R. Agrawal, and R. Srikant Mining generalized association rules VLDB'95 1995 407 419
10. J. Han, and Y. Fu Mining multiple-level association rules in large databases IEEE Transactions on Knowledge and Data Engineering 11 7 1999 798 805
11. E. Baralis, L. Cagliero, T. Cerquitelli, and P. Garza Generalized association rule mining with constraints Information Sciences 194 2012 68 84
12. L. Cagliero, Discovering temporal change patterns in the presence of taxonomies, IEEE Transactions on Knowledge and Data Engineering 25 (no. 3) (March 2013) 541-555, http://dx.doi.org/10.1109/TKDE.2011.233.
13. M. Barsky, S. Kim, T. Weninger, and J. Han Mining flipping correlations from large datasets with taxonomies Proceedings of the VLDB Endowment 5 4 2011 370 381 (URL http://dl.acm.org/citation.cfm?id=2095686.2095695)
14. C. Clifton, R. Cooley, and J. Rennie Topcat: data mining for topic identification in a text corpus IEEE Transactions on Knowledge and Data Engineering 16 8 2004 949 964
15. T. Hofmann The cluster-abstraction model: unsupervised learning of topic hierarchies from text data IJCAI'99 1999 682 687
16. J. Zhu, G. Cheong Fung, and X. Zhou Anddy: a system for author name disambiguation in digital library Database Systems for Advanced Applications 2010 444 447
17. L. Cagliero, A. Fiori, L. Grimaudo, Personalized tag recommendation based on generalized rules, ACM Transactions on Intelligent Systems and Technology (in press).
18. W.L. Database Available at http://wordnet.princeton.edu 2012 (Last Accessed: 01/10/2012. URL http://wordnet.princeton.edu)
19. E. Baralis, S. Chiusano, and P. Garza A lazy approach to associative classification IEEE Transactions on Knowledge and Data Engineering 20 2 2008 156 171
20. F. Thabtah A review of associative classification mining Knowledge Engineering Review 22 1 2007 37 65 10.1017/S0269888907001026.URL http://dx.doi.org/10.1017/S0269888907001026
21. R. Agrawal, T. Imieliński, and A. Swami Mining association rules between sets of items in large databases ACM SIGMOD'93 1993 207 216
22. A. Frank, and A. Asuncion UCI machine learning repository URL http://archive.ics.uci.edu/ml 2010
23. K. Shehzad Simple hybrid and incremental postpruning techniques for rule induction IEEE Transactions on Knowledge and Data Engineering 25 2 2013 476 480
24. F. Thabtah Pruning techniques in associative classification: survey and comparison Journal of Digital Information Management 4 3 2006 197 202
25. J. Han, J. Pei, and Y. Yin Mining frequent patterns without candidate generation SIGMOD'00 2000 1 20
26. W. Li, J. Han, and J. Pei CMAR: accurate and efficient classification based on multiple class-association rules ICDM'01 2001 369 376
27. J. Su, and H. Zhang Full bayesian network classifiers ICML'06 2006 897 904
28. W.W. Cohen Fast effective rule induction ICML'95 1995 115 123
29. D.W. Aha, D.F. Kibler, and M.K. Albert Instance-based learning algorithms Machine Learning 6 1991 37 66
30. U.M. Fayyad, and K.B. Irani Multi-interval discretization of continuous-valued attributes for classification learning International Joint Conference on Artificial Intelligence 1993 1022 1029
31. C.-C. Chang, and C.-J. Lin LIBSVM: a library for support vector machines ACM Transactions on Intelligent Systems and Technology 2 2011 27:1 27:27
32. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques 2005 Morgan Kaufmann (software downloaded from http://www.cs.waikato.ac.nz/ml/weka/)
33. J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 (URL http://dl.acm.org/citation.cfm?id=1248547.1248548)
34. T.G. Dietterich Approximate statistical test for comparing supervised classification learning algorithms Neural Computation 10 7 1998 1895 1923
35. E. Hovy, and C.-Y. Lin Automated text summarization in SUMMARIST Workshop On Intelligent Scalable Text Summarization 1997 18 24
36. L. Cagliero, A. Fiori, Discovering generalized association rules from twitter, Intelligent Data Analysis 17 (4) (in press).
37. O. Zamir, O. Etzioni, O. Madani, and R.M. Karp Fast and intuitive clustering of web documents KDD'97 1997 287 290
38. L. Tao, and S.A. Sarabjot Exploiting domain knowledge by automated taxonomy generation in recommender systems EC-Web'09 2009 120 131
39. E. Baralis, L. Cagliero, T. Cerquitelli, V. D'Elia, and P. Garza Support driven opportunistic aggregation for generalized itemset extraction IEEE IS'10 2010 102 107
40. J. Hipp, A. Myka, R. Wirth, and U. Güntzer A new algorithm for faster mining of generalized association rules PKDD'98 1998 74 82
41. I. Pramudiono, and M. Kitsuregawa FP-tax: tree structure based generalized association rule mining DMKD '04 2004 60 63
42. E. Baralis, L. Cagliero, T. Cerquitelli, P. Garza, and M. Marchetti CAS-MINE: providing personalized services in context-aware applications by means of generalized rules Knowledge and Information Systems 2010 1 28
43. J. Li, G. Dong, K. Ramamohanarao, and L. Wong DeEPs: a new instance-based lazy discovery and classification system Machine Learning 54 2 2004 99 124
44. X. Zhang, G. Dong, and K. Ramamohanarao Information-based classification by aggregating emerging patterns IDEAL'00 2000 48 53
45. R. Rak, L. Kurgan, and M. Reformat A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation Data & Knowledge Engineering 64 1 2008 171 197
46. K. Yu, X. Wu, W. Ding, H. Wang, and H. Yao Causal associative classification ICDM'11 2011 914 923
47. H. Cheng, X. Yan, J. Han, and C. Wei Hsu Discriminative frequent pattern analysis for effective classification ICDE'07 2007 716 725
48. E. Baralis, L. Cagliero, and P. Garza Enbay: a novel pattern-based Bayesian classifier IEEE Transactions on Knowledge and Data Engineering 99 2012 1 10.1109/TKDE.2012.197 (PrePrints)