Hybrid outlier mining algorithm based evaluation of client moral risk in insurance company

ICIME 2010 - 2010 2nd IEEE International Conference on Information Management and Engineering

Volumn 1, Issue , 2010, Pages 585-589

  Xiaoyun, Wang ^a   Danyue, Liu ^a  

^a HANGZHOU DIANZI UNIVERSITY   (China)

Author keywords

Client moral risk; Data mining; Evaluation; Outlier detection; Resolution density based algorithm

Indexed keywords

ADVERSE SELECTION; CLIENT MORAL RISK; DATA MINING ALGORITHM; DATA SETS; DENSITY FACTOR; DENSITY-BASED ALGORITHM; INFORMATION DATA; INPUT PARAMETER; INSURANCE COMPANIES; INSURANCE INDUSTRY; LOSS RATIO; OUTLIER DETECTION; OUTLIER MINING; VIABLE SOLUTIONS;

ALGORITHMS; DETECTORS; EXPERIMENTS; INFORMATION MANAGEMENT; INSURANCE; LAWS AND LEGISLATION; RISK MANAGEMENT;

DATA MINING;

EID: 77954696447     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIME.2010.5478070     Document Type: Conference Paper

References (10)

1. Foss. A, Zaïane. O, "A Parameterless Method for Efficiently Discovering Clusters of Arbitrary Shape in Large Datasets," Proc. IEEE International Corference on Data Mining(ICDM 02), IEEE Press, Dec. 2002, pp. 179-186, doi:10.1109/ICDM.2002.1183901.
2. G. Bart. Baesens, Rudy. Setiono, Christophe. Mues, Jan. Vanthienen, "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, vol. 49, Mar. 2003, pp. 312-329, doi:10.1287/mnsc.12739.
3. Hongqin. Fan, Osmar. R. Zaïane, Andrew. Foss, Junfeng. Wu, "Resolution-Based Outlier Factor: Detecting the Top-n Most Outlying Data Points in Engineering Data," Knowl Inf Syst, vol. 19, Feb. 2009, pp. 31-51, doi: 10.1007/s.10115.
4. Knorr. E, Ng. R, "Algorithms for Mining Distance-Based Outliers in Large Datasets," Proc. International Conference on Very Large Data Bases(ICVLDB 98), Springer Berlin Press, Feb. 1998, pp. 392-403.
5. Yi. Peng, Gang. Kou, Yong. Shi, "Knowledge-Rich Data Mining in Financial Risk Detection," Proc. International Conference on Computational Science (ICCS 09), Springer Berlin Press, May. 2009, pp. 534-54, doi: 10.1007/ICCS.2009.978.
6. Ester. M, Kriegel. H, Sander. J, Xu. X, "A Density-Based Algorithm Fordiscovering Clusters in Large Spatial Databases with Noise," Proc. Conference on Knowledge Discovery and Data Mining (KDD 96), Springer Berlin Press, May. 1996, pp. 226-231.
7. R. J. Kuo, S. Y. Lin, C. W. Shih, "Mining Association Rules Through Integration of Clustering Analysis and Colony System for Health Insurance Database in Taiwan," Expert System with Application, vol. 33, Oct. 2007, pp. 794-808, doi:10.1016/j.eswa.035.
8. Shu. Hsien. Liao, Ya. Ning. Chen, Yu. Yia. Tseng, "Mining Demand Chain Knowledge of Life Insurance Market for New Product Development," Expert System with Application, Jul. 2009, pp. 9422-9437, doi:10.1016/j.eswa. 053.
9. Markos. Markou, Sameer. Singh, "Novelty detection: a review-part 1: statistical approaches," Signal Processing, Jul. 2003, pp. 2481-2497, doi:10.1016/j.sigpro.
10. Gholamhosein. Sheikholeslami, Surojit. Chatterjee, Aidong. Zhang, "WaveCluster: a wavelet-based clustering approach for spatial data in very large databases," Proc. Conference on Very Large Data Base(VLDB 98), Springer Berlin Press, Feb. 2000, pp. 289-304, doi: 10.1007/s007780050009.