Mining rare cases in post-operative pain by means of outlier detection

IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2011

Volumn , Issue , 2011, Pages 035-041

  Ahmed, M U ^a   Funk, P ^a  

^a MÄLARDALEN UNIVERSITY   (Sweden)

Author keywords

case mining; clustering; information technology; medical informatics; post operative pain; rare cases

Indexed keywords

CLUSTERING; CLUSTERING APPROACH; ELECTRONIC PATIENT RECORD; FUZZY C-MEANS ALGORITHMS; HEALTH PROFESSIONALS; MEDICAL INFORMATICS; NON-TRIVIAL; OUTLIER DETECTION; PAIN TREATMENT; PATIENT CASE; POST-OPERATIVE PAIN; RARE CASES;

CLUSTERING ALGORITHMS; COPYING; HEALTH; PATIENT TREATMENT; SIGNAL PROCESSING;

INFORMATION TECHNOLOGY;

EID: 84857871506     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISSPIT.2011.6151532     Document Type: Conference Paper

References (45)

1. nd IEEE International Conference on Data Mining, Maebashi City, Japan, December 2002, page 709,
2. M.U. Ahmed, P. Funk, A Case-Based Retrieval System for Postoperative Pain Treatment, accepted in the International Workshop Case-Based Reasoning CBR 2011, IBaI, Germany, New York/ USA, Editor(s):Petra Perner, September, 2011
3. M.O. Mansur, and M. Noor Md. Sap, Outlier Detection Technique in Data Mining: A Research Perspective, In: Postgraduate Annual Research Seminar 2005.
4. Loureiro L. Torgo, and C. Soares, Outlier Detection Using Clustering Methods: a Data Cleaning Application, In the proceedings of the data mining for business workshop, 2004
5. M.S. Anbarasi, S. Ghaayathri, R. Kamaleswari,and I. Abirami, Outlier Detection for Multidimensional Medical Data, International Journal of Computer Science and Information Technologies, Vol. 2 (1), 2011, 512-516
6. st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007, Madrid, 20-21 Sept. 2007, pp: 443 - 445
7. H. Guldemir, and A. Sengur, Comparison of clustering algorithms for analog modulation classification, Expert Systems with Applications, vol 30, 2006, pp: 642-649 (Pubitemid 43267424)
8. th International IEEE Conference on Fuzzy Systems and Knowledge Discovery, ISBN: 978-0-7695-3735-1/09, 2009
9. G. Chen, S. A. Jaradat, N. Banerjee, T.S. Tanaka, M.S.H. Ko, M.Q. Zhang, Evaluation and comparison of clustering algorithms in anglyzing es cell gene expression data, Statistica Sinica, Vol. 12 2002, pp. 241-262
10. D.B Gordon, R.C Polomano, T.A Pellino, D.C Turk, L.M McCracken, G. Sherwood, J.A Paice, M.S Wallace, S.A Strassels, J.T Farrar, Revised American Pain Society Patient Outcome Questionnaire (APS-POQ-R) for quality improvement of pain management in hospitalized adults: preliminary psychometric evaluation, Journal of Pain,11(11), 2010, pp:1172-86.
11. V. Barnett, T. Lewis, Outliers in Statistical Data, John Wiley, 1994.
12. R. Johnson, Applied Multivariate Statistical Analysis, Prentice Hall, 1992.
13. P. Rousseeuw, A. Leory, Robust Regression & Outlier Detection, Wiley Series, in Probability and Statistics, 1987.
14. M. Markus, K. Hans-Peter, T. Raymond & S. Jörg, LOF: Identifying Density-Based Local Outliers, ACM 2000 1-58113-218-2/00/05.
15. L. Zhide, C. Jiabin, S. Chunlei, A New RBF Neural Network with GA-based Fuzzy C-Means Clustering Algorithm for SINS Fault Diagnosis, Control and Decision Conference, 2009. CCDC '09. Pp: 208 - 211
16. Z. Lin, C. Fu-Lai, & W. Shitong, Generalized Fuzzy C-Means Clustering Algorithm With Improved Fuzzy Partitions, Transaction On Systems, Man & Cybernetics- Part B: Cybernetics, Vol 39, No.3, June 2009, 1083-4419/ 2008.
17. L. Jingwei, & X. Meizhi, Kernelized Fuzzy Attribute C-Means Clustering Algorithm, Fuzzy Sets and Systems, vol. 159, 2008, pp: 2428 - 2445
18. A. Mohanad, M. Mohammad & R. Abdullah, Optimizing of Fuzzy CMeans Clustering Algorithm Using GA, World Academy of Science, Engineering and Technology, vol. 39, 2008
19. C. Somporn, L. Chidchanok, S. Peraphon, & S. Suchada, Fuzzy CMeans: A Statistical Feature Classification of Text and Image Segmentation Method, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS), Vol. 9, Issue. 6, 2001, pp. 661-671
20. T. Velmurugan, T. Santhanam, Performance Evaluation of K-Means and Fuzzy C-Means Clustering Algorithms for Statistical Distributions of Input Data Points, European Journal of Scientific Research, vol.46, No.3, 2010, pp.320-330
21. T. Jeremy, M. Alejandro & S. Werner, Assessment and Pruning of Hierarchical Model Based Clustering, In Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining ACM SIGKDD, 2003
22. S. Haojun, L. Zhihui & K. Lingjun, A Document Clustering Method based on Hierarchical Algorithm with Model Clustering, 22nd International Conference on Advanced Information Networking and Applications-Workshops, 2008
23. F. Clark, Parallel algorithms for hierarchical clustering, Parallel Computing, vol. 21, 1995, pp. 1313-1325
24. st International Conference on Machine Learning and Cybernetics, 4-5 November 2002
25. B. Young-Keun, & L. Chul-Heui, Fuzzy time series prediction using hierarchical clustering algorithms, Expert Systems with Applications, Vol. 38, Issue 4, 2011, pp. 4312-4325
26. R. Saeidi & H. Sadegh, Hierarchical Mixture Clustering Mixture Clustering & Its Application to GMM Based Text Independent Speaker Identification, International Symposium on Telecommunications, 2008, pp. 770-773
27. W. Fang & Q. J Zhang, An improved K-means clustering algorithm and application to combined multi-codebook/MLP neural network speech recognition, Canadian Conference on Electrical and Computer Engineering, 1995, pp. 999-1002.
28. rd International Conference on Genetic and Evolutionary Computing, 2009
29. C. Yiu-Ming, k-Means: A new generalized k-means clustering algorithm, Letters Pattern Recognition, vol. 24, 2003, pp. 2883-2893
30. Q. Dingxi, A Comparative Study of the K-Means Algorithm &Thenormal Mixture Model for Clustering: Bivariate Homoscedastic Case, Journal of Statistical Planning and Inference, vol. 140, 2010, pp. 1701-1711
31. U. Naounori & N. Ryohei, Split & Merge EM Algorithm For Improving Gaussian Mixture Density Estimates, Journal of VLSI Signal Processing, vol. 26, 2000, pp. 133-140
32. S. Ananth & R.R. Venkata, Parameter Tying and Gaussian Clustering for Faster, Better & Smaller Speech Recognition, EUROSPEECH, 1999
33. Y. Zhiwen & W. Hau-San, Fast Gaussian Mixture Clustering For Skin Detection, IEEE International Conference on Image Processing, 2006, pp. 2997 - 3000
34. J.E. Rougui, M. Rziza & D. Aboutajdine, Fast Incremental Clustering of Gaussian Mixture Speaker Models for Scaling up Retrieval in On-Line Broadcast, International Conference on Acoustics, Speech and Signal Processing, 2006
35. K. Jeyhan, An Unsupervised Gaussian Cluster Formation Technique as a Bussgang Blind Deconvolution Algorithm, IEEE International Symposium on Circuits and Systems, 1993, pp. 719-722
36. st International Conference on Emerging Trends in Engineering and Technology, 2008
37. R.H. Eduardo & F. F. Nelson, Using a Clustering Genetic Algorithm for Rule Extraction from Artificial Neural Networks, IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, 2000, pp. 199-206
38. M. Ujjwal, Genetic algorithm-based clustering technique, Pattern Recognition, vol. 33, 2000
39. G. Gautam & B. Chaudhuri, A novel genetic algorithm for automatic clustering, Pattern Recognition, vol. 25, 2004, pp. 173-187 (Pubitemid 38003129)
40. B. Stefan, K. Hans-Peter, & P. Martin, Multi-Step Density-Based Clustering, Knowledge and Information Systems (KAIS), vol.9, No. 3, 2006
41. B. Stefan, K. Hans-Peter & P. Martin, Parallel Density-Based Clustering of Complex Objects, W.K. Ng et al. (eds.): PAKDD 2006, pp.179-188
42. th IEEE Int. Conf. on Data Mining (ICDM 04), Brighton UK, 2004.
43. G. Bhupesh, ART Neural Network Based Clustering Method Produces Best Quality Clusters of Fingerprints in Comparison to Self Organizing Map & K-Means Clustering Algorithms, International Conference on Innovations in Information Technology, 2008, pp. 282-286
44. W.L. Tung & C. Quek, Falcon: Neural Fuzzy Control & Decision Systems Using FKP and PFKP Clustering Algorithms, Transactions On Systems, Man & Cybernetics-Part B: Cybernetics, vol. 34, No. 1, 2004
45. A. Ioannis, W. Phil, A. Trinder, NOCEA: A rule-based evolutionary algorithm for efficient and effective clustering of massive high-dimensional databases, Applied Soft Computing, vol. 7, 2007, pp. 668-710.