Mining Open Answers in Questionnaire Data

IEEE Intelligent Systems

Volumn 17, Issue 5, 2002, Pages 58-63

  Yamanishi, Kenji ^a   Li, Hang ^b  

^a NEC CORPORATION   (Japan)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMOBILES; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; MORPHOLOGY; STATISTICAL METHODS; SURVEYS;

BRAND IMAGE; NATURAL LANGUAGE; STATISTICAL LEARNING; STOCHASTIC COMPLEXITY; SURVEY ANALYSIS SYSTEM; TEXT MINING;

DATA MINING;

EID: 0141502341     PISSN: 15411672     EISSN: None     Source Type: Journal    
DOI: 10.1109/MIS.2002.1039833     Document Type: Article

References (10)

1. H. Li and K. Yamanishi, “Mining from Open Answers in Questionnaire Data,” Proc. Int'l Conf. Knowledge Discovery and Data Mining (KDD01), ACM Press, New York, 2001, pp. 443-449.
2. J. Rissanen, “Fisher Information and Stochastic Complexity,” IEEE Trans. Information Theory, vol. 42, no. 1, Jan. 1996, pp. 40-47.
3. J. Rissanen, “Stochastic Complexity in Learning,” J. Computer Systems and Sciences, vol. 55, no. 1, Aug. 1997, pp. 89-95.
4. K. Yamanishi, “A Decision-Theoretic Extension of Stochastic Complexity and Its Applications to Learning,” IEEE Trans. Information Theory, vol. 44, no. 4, July 1998, pp. 1424-1439.
5. H. Li and K. Yamanishi, “Text Classification Using ESC-Based Stochastic Decision Lists,” Information Processing and Management, vol. 38, no. 3, 2002, pp. 343-361.
6. K. Yamanishi, “A Learning Criterion for Stochastic Rules,” Machine Learning, vol. 9, 1992, pp. 165-203.
7. J.R. Quinlan, C4.5 Programs for Machine Learning, Morgan Kaufmann, San Francisco, 1993.
8. R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. Int'l Conf. Very Large Data Bases(VLDB 94), Morgan Kaufmann, San Francisco, 1994, pp. 487-499.
9. J.P. Benzecri, Correspondence Analysis Handbook, Mercel Dekker, New York, 1992.
10. S. Morinaga et al., “Mining Product Reputations on the Web,” Proc. Int'l Conf. Knowledge Discovery and Data Mining (KDD 02), ACM Press, New York, 2002, pp. 341-349.