An overview of the use of neural networks for data mining tasks

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Volumn 2, Issue 3, 2012, Pages 193-208

  Stahl, Frederic ^a   Jordanov, Ivan ^a  

^a BOURNEMOUTH UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMIMETICS; CLUSTER ANALYSIS; COMPUTER AIDED DIAGNOSIS; LARGE DATASET; NEURAL NETWORKS; PATTERN RECOGNITION;

BIOLOGICALLY INSPIRED; COMPLEX DATA; DATA MINING TASKS; EXTRACTING INFORMATION; FINANCIAL DATA ANALYSIS; LARGE DATASETS; SUPERVISED AND UNSUPERVISED LEARNING;

DATA MINING;

EID: 84873148398     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.1052     Document Type: Article

References (91)

1. Witten IH, Eibe F. Data Mining: practical Machine Learning Tools and Techniques. 2nd ed. San Francisco, CA: Morgan Kaufmann; 2005
2. Way J, Smith EA. The evolution of synthetic aperture radar systems and their progression to the eos sar. IEEE Trans Geosc Remote Sens 1991, 29:962-985
3. Human Genome Project Information. Available at: www.ornl.gov/sci/techresources/Human Genome/ home.shtml (Accessed December 20, 2011)
4. Stahl F, Bramer M, Adda M. Parallel induction of modular classification rules. In: Bramer M, Coenen F, Petridis M, eds. Twenty-Eighth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. England: Springer, Cambridge; 2008, 343-348
5. Han J, Kamber M. Data Mining: concepts and Techniques. 3rd ed. San Francisco, CA: Morgan Kaufmann; 2011
6. Bramer MA. Principles of Data Mining. London, United Kingdom: Springer; 2007
7. Lisboa PJG, Vellido A, Edisbury B. Business Applications of Neural Networks: the State-Of-The-Art of Real-World Applications. Singapore: World Scientific; 2000, 7
8. An Introduction to Data Mining. Available at: http://www.thearling.com/text/dmwhite/dmwhite.htm (accessed December 20, 2011)
9. Shen A, Tong R, Deng Y. Application of classification models on credit card fraud detection. In: International Conference on Service Systems and Service Management. Chengdu, China: 2007, 1-4
10. Zhang D, Jiang Q, Li X. Application of neural networks in financial datamining. Int J Compu Intel 2004, 392-395
11. Bate A, Lindquist M, Edwards IR, Olsson S, Orre R, Lansner A, De Freitas RM. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clinic Pharmacol 1998, 54:315-321
12. Surjandari I, Seruni AC. Design of product placement layout in retail shop using market basket analysis. MAKARA Technol Series 2005, 9:43-47
13. Huang C, Chen M, Wang C. Credit scoring with a data mining approach based on support vector machines. Expert Syst Appl 2007, 33:847-856
14. Pei JS, Mai EC. Neural network initialization for modeling nonlinear functions in engineering mechanics. In Proceedings of the 24rd International Modal Analysis Conference. St. Louis, Missouri; 2006
15. Quinlan RJ. C4.5: programs for machine learning. San Francisco: Morgan Kaufmann; 1993
16. Mitchell M. An Introduction to Genetic Algorithms. Cambridge, MA: MIT Press; 2001
17. Ni X. Research of data mining based on neural networks. World Acad Sci, Eng Technol 2008, 39:381- 384
18. Bishop C. Pattern Recognition and Machine Learning. Springer; 2006
19. Jordanov I, Georgieva A. Neural Network Learning with global heuristic optimisation. IEEE Trans Neural Netw 2007, 18:937-942
20. Alpaydin E. Introduction to Machine Learning. 2nd ed. Cambridge, MA: MIT Press; 2010
21. Rumelhart DE, Hinton GE, Williams RJ. Learning Internal Representations by Error Propagation. Cambridge, USA: MIT Press; 1986
22. Widrow B, Hoff ME. Adaptive switching circuits. IRE WESCON Convention Record 1960, 96-104
23. Kohonen T. Self-organized formation of topologically correct feature maps. Biol Cybern 1982, 43:59- 69
24. Kirkpatrick S, Gelatt CD, Vecchi JR, Vecchi MP. Optimization by simulated annealing. Science 1983, 220:671-680
25. Blake CL, Merz CJ. UCI Repository ofMachine LearningDatabases. University of California, Irvine: Department of Information and Computer Sciences; 1998
26. Howlett RJ, Jain LC. Radial Basis Function Networks 1: recent Developments in Theory and Applications. Heidelberg: Physica-Verlag; 2001
27. Cendrowska J. PRISM: an algorithm for inducing modular rules. Int J Man-Machine Studies 1987, 27:349- 370
28. Steinwart I, Christmann A. Support Vector Machines. New York: Springer; 2008
29. Kernel Machines Software Links. Available at: http://www.kernel-machines.org/software (accessed December 20, 2011)
30. Chen C, Chen L, Zou X, Cai P. Prediction of protein secondary structure content by using the concept of Chou's pseudo amino acid composition and support vector machine. Protein Peptide Lett 2009, 16:27-31
31. Widodo A, Yang BS. Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 2007, 21:2560-2574
32. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw 1989, 2:359-366
33. Shafer JC, Agrawal R, Metha M. SPRINT: a scalable parallel classifier for data mining. In: Proceedings of the 22nd International Conference on Very Large Databases. San Francisco, CA: Morgan Kaufmann; 1996, 544-555
34. Lu H, Setiono R, LiuH.Neurorule: A connectionist approach to data mining. In: Proceedings of 21st International Conference on Very Large Data Bases. Zurich, Switzerland: Morgan Kaufmann; 1995, 478-489
35. Özbaki{dotless}r L, Baykasoǧlu A, Kulluk S. Rule extraction from neural networks via ant colony algorithm for data mining applications. In: Maniezzo V, Battiti R, Watson J-P, eds. Learning and intelligent Optimization. Trento, Italy: Springer; 2008, 177-191
36. Kahramanli H, Allahverdi N. Rule extraction from trained adaptive neural networks using artificial immune systems. Expert Syst Appl 2007, 36:1513-1522
37. Cortez P, Embrechts MJ. Opening black box data mining models using sensitivity analysis. In Symposium on Computational Intelligence and DataMining (CIDM). Paris, France: IEEE; 2011, 341-348
38. Lancashire LJ, Lemetre C, Ball GR. An introduction to artificial neural networks in bioinformatics- application to complex microarray and mass spectrometry datasets in cancer studies. Briefings Bioinformatics 2009, 10:315-329
39. Paliwal M, Kumar UA. Neural networks and statistical techniques: a review of applications. Expert Syst Appl 2009, 36:2-17
40. Xu R, Wunsch D. Survey of clustering algorithms. IEEE Trans Neural Netw 2005, 16:645-678
41. Guyon I, Gunn S, Nikravesh M, Zadeh L. Feature Extraction, Foundations and Applications. Berlin, Heidelberg, Germany: Springer; 2006
42. Kaufmann L, Rousseeuw PJ. Finding Groups in Data: In Introduction to Cluster Analysis. New York: John Wiley & Sons; 1990
43. Dřaghici S. Data Analysis Tools for DNA Microarrays. Boca Raton, FL: Chapman&Hall/CRC; 2003
44. Hogeweg P. The roots of bioinformatics in theoretical biology. PLoS Comput Biol 2011, 7:e1002021. doi:10.1371/journal.pcbi.1002021
45. Alberts B, Bray D, Hopkin K, Johnson A, Lewis J, Raff M,Roberts K, Walter P. Essential Cell Biology. 3rd ed. Garland Science; 2009
46. Lockhart DJ,Winzeler EA. Genomics, gene expression and DNA arrays. Nature 2000, 405:827-836
47. DNA Sequencing. Available at: http://www.dnasequencing.com/ (accessed December 20, 2011)
48. Shendure J, Ji H. Next-generation DNA sequencing. Nat Biol 2008, 26:1135-1145
49. Ray SS, Bandyopadhyay S, Mitra P, Pal SK. Bioinformatics in neurocomputing framework. IEE Proc- Circuits, Devices Syst 2005, 152:556-564
50. Dřaghici S, Potter RB. Protecting HIV drug resistance with neural networks. Bioinformatics 2003, 19:98- 107
51. Herrero J, Valencia A, Dopazo J. A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 2001, 17:126- 136
52. Ma S, Huang J. Penalized feature selection and classification in bioinformatics. Briefings Bioinformatics 2008, 9:392-403
53. Arakawa M, Hasegawa K, Funatsu K. Application of the novel molecular alignment method using the hopfield neural network to 3d-qsar. J Chem Inform Comp Sci 2003, 43:1396-1402
54. JonesDT. Genthreader: an efficient and reliable protein fold recognition method for genomic sequences. J Mol Biol 1999, 287:797-815
55. Pedersen AG, Baldi P, Chauvin Y, Brunak S. The biology of eukaryotic promoter prediction: a review. Comput Chem 1999, 23:191-207
56. Lukashin AV, Anshelevich VV, Amirikyan BR, Gragerov AI, Frank-Kamenetskii MD. Neural network models for promoter recognition. J Biomol Struct Dyn 1989, 6:1123-1133
57. Kalate RN, Tambe SS, Kulkarni BD. Artificial neural networks for prediction of mycobacterial promoter sequences. Comput Biol Chem 2003, 27:555-564
58. Abeel T, Saeys Y, Rouzé P, Van de Peer Y. ProSOM: core promoter prediction based on unsupervised clustering of DNA physical profiles. Bioinformatics 2008, 24:24-31
59. Quackenbush J. Computational genetics: computational analysis of microarray data. Nat Rev Genet 2001, 2:418-427
60. O'Neill M, Song L. Neural network analysis of lymphoma microarray data: prognosis and diagnosis nearperfect. BMC Bioinformatics 2003, 4. Available at: http://www.ncbi.nlm.nih.gov/pubmed/12697066
61. Ando T, Suguro M, Hanai T, Kobayashi T, Honda H, Seto M. Fuzzy neural network applied to gene expression profiling for predicting the prognosis of diffuse large b-cell lymphoma. Cancer Sci 2002, 93:1207- 1212
62. Wei JS, Greer BT, Westermann F, Steinberg SM, Son CG, Chen QR, Whiteford CC, Bilke S, Krasnoselsky AL, Cenacchi N, et al. Prediction of clinical outcomeusing gene expression profiling and artificial neural networks for patients with neuroblastoma. Cancer Res 2004, 64:6883-6891
63. Ressom H, Wang D, Natarajan P. Clustering gene expression data using adaptive double self-organizing map. Physiol Genom 2003, 14:35-46
64. Ritchie MD, White BC, Parker JS, Hahn LW, Moore JH. Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics 2003, 4. Available at: http://www.ncbi.nlm.nih.gov/pubmed/12846935
65. Berrar D, Stahl F, Silva CS, Rodrigues JR, Brito RM. Towards data warehousing and mining of protein unfolding simulation data. J Clinic Monitor Comput 2005, 19:307-317
66. Riis SK, Krogh A. Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments. J Comput Biol 1996, 3:163-183
67. Kaur H, Raghava GP. A neural network method for prediction of beta-turn types in proteins using evolutionary information. Bioinformatics 2004, 20:2751- 2758
68. Li X, Hu H, Shu L. Predicting human immunodeficiency virus protease cleavage sites in nonlinear projection space. Mol Cell Biochem 2010, 339:127-33
69. Huang W, Lai KK, Nakamori Y,Wang S, Yu L. Neural Networks in Finance and Economics Forecasting. Int J Informat Technol Decision Making (IJITDM) 2007, 6:113-140
70. Refenes AN, Zapranis A, Francis G. Stock performance modeling using neural networks: a comparative study with regression models. Neural Netw 1994, 7:375- 388
71. Pino R, Parreno J, Gomez A, Priore P. Forecasting nextday price of electricity in the spanish energy market using artificial neural networks. Eng Appl Artif Intell 2008, 21:53-62
72. Atsalakis GS, Valavanis KP. Surveying stock market forecasting techniques-Part II: soft computing methods. Expert Syst Appl 2009, 36:5932-5941
73. Tan YH, Thoen W. An outline of a trust model for electronic commerce. Appl Artif Intell 2000, 14:849- 862
74. Zaslavsky B, Strizhak A. Credit card fraud detection using self-organizing maps. Inf Secur 2006, 18:48-63
75. Quah JTS, Sriganesh M. Real-time credit card fraud detection using computational intelligence. Expert Syst Appl 2008, 35:1721-1732
76. Islam MS, Zhou L, Li F. Application of Artificial Intelligence (Artificial Neural Network) to Assess Credit Risk: A Predictive Model For Credit Card Scoring.MSc Thesis, Blekinge Institute of Technology; 2009
77. Limsombunchai V, Gan C, LeeM. An analysis of credit scoring for agricultural loans in Thailand. Am J Appl Sci 2005, 2:1198-1205
78. Hua Z, Wang Y, Xu X, Zhang B, Liang L. Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Syst Appl 2007, 33:434-440
79. Chen WS, Du YK. Using neural networks and data mining techniques for the financial distress prediction model. Expert Syst Appl 2009, 36:4075-4086
80. Tsai CF, Wu JW. Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Syst Appl 2008, 34:2639-2649
81. Simić D, Kovǎcević I, Simić S. Insolvency prediction for assessing corporate financial health. Logic J IGPL 2011. doi:10.1093/jigpal/jzr009
82. Paliwal M, Kumar UA. Review: neural networks and statistical techniques: a review of applications. Expert Syst Appl 2009, 36:2-17
83. Lee WI, Shih BY, Chung YS. The exploration of consumers' behavior in choosing hospital by the application of neural network. Expert Syst Appl 2008, 34:806-816
84. Hung C, Tsai CF. Market segmentation based on hierarchical self-organizingmap formarkets of multimedia on demand. Expert Syst Appl 2008, 34:780-787
85. Chiang WK, Zhang D, Zhou L. Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression. Decis Support Syst 2006, 41:514-531
86. Ekonomou L. Greek long-term energy consumption prediction using artificial neural networks. Energy 2010, 35:512-517
87. Ekici BB, Aksoy UT. Prediction of building energy consumption by using artificial neural networks. Adv Eng Software 2009, 40:356-362
88. Li G, Shi J. On comparing three artificial neural networks for wind speed forecasting. Appl Energy 2010, 87:2313-2320
89. Azadeh A, Maghsoudi A, Sohrabkhani S. An integrated artificial neural networks approach for predicting global radiation. Energy Conversion Manage 2009, 50:1497-1505
90. Mevawalla ZN, May GS, Honjo M, Kiehlbauch MW. Neural network modeling of fabrication yield using manufacturing data. In Annual Advanced Semiconductor Manufacturing Conference (ASMC). Saratoga Springs, NY: IEEE, Saratoga Springs; 2011, 1-6
91. Castanheira LG, Vasconcelos JA, Reis AJR,Magalhaes PHV, Da Silva SAL. Application of neural networks in the classification of incipient faults in power transformers: A study of case. In: International Joint Conference on Neural Networks. San Jose, CA: IEEE; 2011