A MapReduce solution for associative classification of big data

Information Sciences

Volumn 332, Issue , 2016, Pages 33-55

  Bechini, Alessio ^a   Marcelloni, Francesco ^a   Segatori, Armando ^a  

^a UNIVERSITY OF PISA   (Italy)

Author keywords

Associative classifiers; Big data; Cluster computing frameworks; MapReduce

Indexed keywords

ALGORITHMS; ASSOCIATION RULES; BIG DATA; CLUSTER COMPUTING; LEARNING ALGORITHMS;

ASSOCIATIVE CLASSIFICATION; ASSOCIATIVE CLASSIFIERS; CLASSIFICATION ASSOCIATION RULES; COMPUTING FRAMEWORKS; DISTRIBUTED LEARNING ALGORITHMS; MAP-REDUCE; MAP-REDUCE PROGRAMMING; RULE-BASED CLASSIFICATION;

CLASSIFICATION (OF INFORMATION);

EID: 84961990876     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2015.10.041     Document Type: Article

References (70)

1. Apache Drill. Accessed: October 2015 (http://drill.apache.org).
2. Apache Hadoop. Accessed: October 2015 (http://hadoop.apache.org).
3. Apache Mahout. Accessed: October 2015 (http://mahout.apache.org).
4. Apache MLlib.Accessed: October 2015 (https://spark.apache.org/mllib/).
5. Apache Spark. Accessed: October 2015, (https://spark.apache.org).
6. Apache Storm. Accessed: October 2015 (https://storm.apache.org).
7. Vowpal wabbit. Accessed: October 2015 (http://hunch.net/~vw/).
8. N. Abdelhamid, A. Ayesh, F. Thabtah, S. Ahmadi, and W. Hadi MAC: a multiclass associative classification algorithm J. Inf. Knowl. Manag. 11 02 2012 10.1142/S0219649212500116
9. R. Agrawal, T. Imieliński, and A. Swami Mining association rules between sets of items in large databases SIGMOD Rec. 22 2 1993 207 216 10.1145/170036.170072
10. M.I.A. Ajlouni, W. Hadi, and J. Alwedyan Detecting phishing websites using associative classification Eur. J. Bus. Manag. 5 15 2013 36 40
11. N.K. Alham, M. Li, Y. Liu, and S. Hammoud A MapReduce-based distributed SVM algorithm for automatic image annotation Comput. Math. Appl. 62 7 2011 2801 2811 10.1016/j.camwa.2011.07.046
12. E. Baralis, and P. Garza A lazy approach to pruning classification rules Proceedings of 2002 IEEE International Conference on Data Mining 2002 35 42 10.1109/ICDM.2002.1183883
13. A. Bifet, G. Holmes, R. Kirkby, and B. Pfahringer MOA: massive online analysis J. Mach. Learn. Res. 11 2010 1601 1604
14. D. Boyd, and K. Crawford Critical questions for big data Inf. Commun. Soc. 15 5 2012 662 679 10.1080/1369118X.2012.678878
15. L. Breiman Random forests Mach. Learn. 45 1 2001 5 32
16. G. Buehrer, S. Parthasarathy, S. Tatikonda, T. Kurc, and J. Saltz Toward terabyte pattern mining: An architecture-conscious solution Proceedings of the 12th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP '07 2007 ACM New York, NY, USA 2 12 10.1145/1229428.1229432
17. G. Caruana, M. Li, and M. Qi A MapReduce based parallel SVM for large scale spam filtering Proceedings of 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) vol. 4 2011 2659 2662 10.1109/FSKD.2011.6020074
18. H. Chen, R.H.L. Chiang, and V.C. Storey Business intelligence and analytics: from big data to big impact MIS Q. 36 4 2012 1165 1188
19. X.-Q. Cheng, X.-L. Jin, Y.-Z. Wang, J.-F. Guo, T.-Y. Zhang, and G.-J. Li Survey on big data system and analytic technology Ruan Jian Xue Bao/J. Softw. 25 9 2014 1889 1908 10.13328/j.cnki.jos.004674
20. C.-T. Chu, S.K. Kim, Y.-A. Lin, Y. Yu, G.R. Bradski, A.Y. Ng, and K. Olukotun Map-Reduce for machine learning on multicore B. Schölkopf, J.C. Platt, T. Hoffmanan, Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems Vancouver, British Columbia, Canada, December 4-7, 2006 2006 MIT Press 281 288
21. E.J. Clarke, and B.A. Barton Entropy and MDL discretization of continuous variables for bayesian belief networks Int. J. Intell. Syst. 15 1 2000 61 92
22. G. Costa, R. Ortale, and E. Ritacco X-Class: associative classification of XML documents by structure ACM Trans. Inf. Syst. 31 1 2013 3:1 3:40 10.1145/2414782.2414785
23. A. De Mauro, M. Greco, and M. Grimaldi What is big data? A consensual definition and a review of key research topics AIP Conf. Proc. 1644 1 2015 97 104 10.1063/1.4907823
24. J. Dean, and S. Ghemawat MapReduce: simplified data processing on large clusters Commun. ACM 51 1 2008 107 113 10.1145/1327452.1327492
25. J. Dean, and S. Ghemawat MapReduce: a flexible data processing tool Commun. ACM 53 1 2010 72 77 10.1145/1629175.1629198
26. J. Dougherty, R. Kohavi, and M. Sahami Supervised and unsupervised discretization of continuous features Proceedings of the Twelfth International Conference on Machine Learning 1995 Morgan Kaufmann 194 202
27. S. Dua, H. Singh, and H. Thompson Associative classification of mammograms using weighted rules Expert Systems with Applications 36 5 2009 9250 9259 10.1016/j.eswa.2008.12.050
28. W. Fan, and A. Bifet Mining big data: current status, and forecast to the future SIGKDD Explor. Newsl. 14 2 2013 1 5 10.1145/2481244.2481246
29. U.M. Fayyad, and K.B. Irani Multi-interval discretization of continuous-valued attributes for classification learning Proceedings of IJCAI 1993 1022 1029
30. J. Han, J. Pei, Y. Yin, and R. Mao Mining frequent patterns without candidate generation: a frequent-pattern tree approach Data Min. Knowl. Discov. 8 1 2004 53 87 10.1023/B:DAMI.0000005258.31418.83
31. Q. He, C. Du, Q. Wang, F. Zhuang, and Z. Shi A parallel incremental extreme SVM classifier Neurocomputing 74 16 2011 2532 2540
32. H. Hu, Y. Wen, T.-S. Chua, and X. Li Toward scalable systems for big data analytics: a technology tutorial IEEE Access 2 2014 652 687 10.1109/ACCESS.2014.2332453
33. J.P.J. Han, and M. Kamber Data Mining: Concepts and Techniques 3rd ed. Data Management Systems 2012 Morgan Kaufmann
34. X. Jin, B. Wah, X. Cheng, and Y. Wang Significance and challenges of big data research Big Data Res. 2 2 2015 59 64 10.1016/j.bdr.2015.01.006
35. S. Kotsiantis, and D. Kanellopoulos Discretization techniques: a recent survey GESTS International Trans. Comput. Sci. Eng. 32 1 2006 47 58
36. D. Laney 3D Data Management: Controlling Data Volume, Velocity, and Variety Technical Report 2001 META Group
37. H. Li, Y. Wang, D. Zhang, M. Zhang, and E.Y. Chang PFP: parallel FP-Growth for query recommendation Proceedings of the 2008 ACM Conference on Recommender Systems, RecSys '08 2008 ACM New York, NY, USA 107 114 10.1145/1454008.1454027
38. L. Li, and M. Zhang The strategy of mining association rule based on cloud computing Proceedings of the 2011 International Conference on Business Computing and Global Informatization, BCGIn 2011 2011 475 478 10.1109/BCGIn.2011.125
39. W. Li, J. Han, and J. Pei CMAR: accurate and efficient classification based on multiple class-association rules Proceedings of IEEE International Conference on Data Mining 2001 2001 369 376 10.1109/ICDM.2001.989541
40. M.-Y. Lin, P.-Y. Lee, and S.-C. Hsueh Apriori-based frequent itemset mining algorithms on MapReduce Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC '12 2012 ACM New York, NY, USA 76:1 76:8 10.1145/2184751.2184842
41. B. Liu, W. Hsu, and Y. Ma Integrating classification and association rule mining Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining 1998 80 86
42. B. Liu, Y. Ma, and C.-K. Wong Classification using association rules: weaknesses and enhancements R. Grossman, C. Kamath, P. Kegelmeyer, V. Kumar, R. Namburu, Data Mining for Scientific and Engineering Applications Massive Computing vol. 2 2001 Springer US 591 605 10.1007/978-1-4615-1733-7-30
43. L. Liu, E. Li, Y. Zhang, and Z. Tang Optimization of frequent itemset mining on multiple-core processor Proceedings of the 33rd International Conference on Very Large Data Bases, VLDB '07 2007 VLDB Endowment 1275 1285
44. V. López, S. Del Río, J. Benítez, and F. Herrera Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data Fuzzy Sets Syst. 258 2015 5 38 10.1016/j.fss.2014.01.015
45. S.O.M. Snir MPI-The Complete Reference: The MPI Core 1998 MIT Press
46. D.T. Marr, F. Binns, D.L. Hill, G. Hinton, D.A. Koufaty, A.J. Miller, and M. Upton Hyper-Threading technology architecture and microarchitecture Intel Technol. J. 6 1 2002 4 15
47. S. Melnik, A. Gubarev, J.J. Long, G. Romer, S. Shivakumar, M. Tolton, and T. Vassilakis Dremel: interactive analysis of web-scale datasets Proc. VLDB Endow. 3 1-2 2010 330 339 10.14778/1920841.1920886
48. M. Minelli, M. Chambers, and A. Dhiraj Big Data Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses 2013 John Wiley & Sons
49. L. Neumeyer, B. Robbins, A. Nair, and A. Kesari S4: distributed stream computing platform Proceedings of 2010 IEEE International Conference on Data Mining Workshops (ICDMW), 2010 170 177 10.1109/ICDMW.2010.172
50. L.T. Nguyen, B. Vo, T.-P. Hong, and H.C. Thanh CAR-miner: an efficient algorithm for mining class-association rules Expert Syst. Appl. 40 6 2013 2305 2311 10.1016/j.eswa.2012.10.035
51. I. Palit, and C.K. Reddy Scalable and parallel boosting with MapReduce IEEE Trans. Knowl. Data Eng. 24 10 2012 1904 1916
52. I. Pramudiono, and M. Kitsuregawa Parallel FP-Growth on PC cluster Advances in Knowledge Discovery and Data Mining Lecture Notes in Computer Science 2637 2003 Springer Berlin Heidelberg 467 473 10.1007/3-540-36175-8-47
53. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann Publishers Inc. San Francisco, CA, USA
54. S. Owen, R. Anil, T. Dunning, and E. Friedman Mahout in Action 2011 Manning Publications Co.
55. Y. Sun, Y. Wang, and A.K. Wong Boosting an associative classifier IEEE Trans. Knowl. Data Eng. 18 7 2006 988 992 10.1109/TKDE.2006.105
56. F. Thabtah A review of associative classification mining Knowl. Eng. Rev. 22 1 2007 37 65 10.1017/S0269888907001026
57. I. Triguero, D. Peralta, J. Bacardit, S. García, and F. Herrera MRPR: A MapReduce solution for prototype reduction in big data classification Neurocomputing 150 PA 2015 331 345 10.1016/j.neucom.2014.04.078
58. A. Veloso, W. Meira Jr., and M.J. Zaki Lazy associative classification Proceedings of the Sixth International Conference on Data Mining, ICDM '06 2006 IEEE Computer Society Washington, DC, USA 645 654 10.1109/ICDM.2006.96
59. S. Wedyan Review and comparison of associative classification data mining approaches Int. J. Comput. Electr. Autom. Control Inf. Eng. 8 1 2014 34 45
60. T. White Hadoop: The Definitive Guide 3rd ed. 2012 O'Reilly Media, Inc
61. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques 3rd Morgan Kaufmann Series in Data Management System 2011 Morgan Kaufmann
62. J. Xin, Z. Wang, L. Qu, and G. Wang Elastic extreme learning machine for big data classification Neurocomputing 149 PA 2015 464 471 10.1016/j.neucom.2013.09.075
63. X. Yin, and J. Han CPAR: Classification based on predictive association rules Proceedings of the third SIAM International Conference on Data Mining 2003 331 335 10.1137/1.9781611972733.40
64. Y. Yoon, and G.G. Lee Two scalable algorithms for associative text classification Inf. Process. Manag. 49 2 2013 484 496 10.1016/j.ipm.2012.09.003
65. H. Yuwen Cost-sensitive incremental classification under the mapreduce framework for mining imbalanced massive data streams J. Discrete Math. Sci. Cryptogr. 18 2015 177 194 10.1080/09720529.2015.1013709
66. M. Zaharia, M. Chowdhury, M.J. Franklin, S. Shenker, and I. Stoica Spark: Cluster computing with working sets Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud'10 2010 USENIX Association Berkeley, CA, USA
67. O. Zaiane, M. El-Hajj, and P. Lu Fast parallel association rule mining without candidacy generation Proceedings IEEE International Conference on Data Mining, 2001. ICDM 2001, 2001 665 668 10.1109/ICDM.2001.989600
68. C. Zhang, F. Li, and J. Jestes Efficient parallel kNN joins for large data in MapReduce Proceedings of the 15th International Conference on Extending Database Technology, EDBT'12 2012 ACM New York, NY, USA 38 49 10.1145/2247596.2247602
69. W. Zhao, H. Ma, and Q. He Parallel K-Means clustering based on MapReduce Cloud Computing Lecture Notes in Computer Science 5931 2009 Springer Berlin Heidelberg 674 679 10.1007/978-3-642-10665-1-71
70. L. Zhou, Z. Zhong, J. Chang, J. Li, J. Huang, and S. Feng Balanced parallel FP-Growth with MapReduce Proceedings of 2010 IEEE Youth Conference on Information Computing and Telecommunications (YC-ICT) 2010 243 246 10.1109/YCICT.2010.5713090