A stock recommendation system exploiting rule discovery in stock databases

Information and Software Technology

Volumn 51, Issue 7, 2009, Pages 1140-1149

  Ha, You Min ^a   Park, Sanghyun ^a   Kim, Sang Wook ^b   Won, Jung Im ^b   Yoon, Jee Hee ^c  

^a Yonsei University   (South Korea)

^b Hanyang University   (South Korea)

^c Hallym University   (South Korea)

Author keywords

Rule discovery; Rule matching; Stock databases

Indexed keywords

FREQUENT PATTERNS; RECOMMENDATION SYSTEMS; RULE DISCOVERY; RULE MATCHING; STOCK DATABASES; STOCK DATUM; STOCK PRICES;

INVESTMENTS;

EID: 67349210013     PISSN: 09505849     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.infsof.2008.06.004     Document Type: Article

References (22)

1. R. Agrawal, C. Faloutsos, A. Swami, Efficient similarity search in sequence databases, in: Proceedings of the International Conference on Foundations of Data Organization and Algorithms (FODO), October 1993, pp. 69-84.
2. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proceedings of the International Conference on Very Large Data Bases, VLDB, 1994, pp. 487-499.
3. R. Agrawal, K. Lin, H.S. Sawhney, K. Shim, Fast similarity search in the presence of noise, scaling, and translation in time-series databases, in: Proceedings of the International Conference on Very Large Data Bases, VLDB, September 1995, pp. 490-501.
4. R. Agrawal, R. Srikant, Mining sequential patterns, in: Proceedings of the International Conference on Data Engineering, 1995, pp. 3-14.
5. Anderson T. The Statistical Analysis of Time Series (1971), Wiley
6. Bloomfield P. Fourier Analysis of Time Series (2000), Wiley
7. W.W. Chu, K. Chiang, Abstraction of high level concepts from numerical values in databases, in: Proceedings of the AAAI Workshop on Knowledge Discovery in Databases, 1994, pp. 133-144.
8. G. Das, K.-I. Lin, H. Mannila, G. Renganathan, P. Smyth, Rule discovery from time series, in: Proceedings of the International Conference on Knowledge Discovery and DataMining, 1998, pp. 16-22.
9. C. Faloutsos, M. Ranganathan, Y. Manolopoulos, Fast subsequence matching in time-series databases, in: Proceedings of the International Conference on Management of Data, ACM SIGMOD, May 1994, pp. 419-429.
10. Guha S., Rastogi R., and Shim K. CURE: an efficient clustering algorithm for large databases. Information Systems 26 1 (2001) 35-58
11. Han J., and Kamber M. Data Mining: Concepts and Techniques (2001), Morgan Kaufman
12. S.W. Kim, S.H. Park, W.W. Chu, An index-based approach for similarity search supporting time warping in large sequence databases, in: Proceedings of the International Conference on Data Engineering, IEEE ICDE, 2001, pp. 607-614.
13. Koscom Data Mall, , 2005.
14. Loh W.K., Kim S.W., and Whang K.Y. A subsequence matching algorithm that supports normalization transform in time-series databases. Data Mining and Knowledge Discovery Journal 9 1 (2004) 5-28
15. S.H. Park, W.W. Chu, J. Yoon, C. Hsu, Efficient searches for similar subsequences of difference lengths in sequence databases, in: Proceedings of the International Conference on Data Engineering, IEEE ICDE, 2000, pp. 23-32.
16. Park S., and Chu W.W. Discovering and matching elastic rules from sequence databases. Fundamenta Informaticae 47 1-2 (2001) 75-90
17. Saad E., Prokhorov D., and Wunsch II D. Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks (1998) 1456-1470
18. Wah B., and Qian M. Constrained formulations and algorithms for stock-price predictions using recurrent FIR neural networks. AAAI/IAAI (2002) 211-216
19. H. White, Economic prediction using neural networks: the case of IBM daily stock returns, in: Proceedings of the IEEE International Conference on Neural Networks, 1988, pp. 451-458.
20. T. Fu, T. Cheung, F. Chung, C. Ng, An innovative use of historical data for neural network based stock prediction, in: Proceedings of the International Conference on Information Sciences, JCIS, October 2006.
21. Y. Kwon, S. Choi, B. Moon, Stock prediction based on financial correlation, in: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO, June 2005, pp. 2061-2066.
22. V. Sehgal, C. Song, SOPS: stock prediction using web sentiment, in: Proceedings of the International Conference on Data Mining Workshop, IEEE ICDMW, October 2007, pp. 21-26.