Rule discovery and probabilistic modeling for onomastic data

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2838, Issue , 2003, Pages 291-302

  Leino, Antti ^a,b   Mannila, Heikki ^a,c   Pitkänen, Ritva Liisa ^a,b  

^a UNIVERSITY OF HELSINKI   (Finland)

^b Res Inst for the Lang of Finland   (Finland)

^c AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

BIODIVERSITY; COMPUTATIONAL METHODS; COMPUTER SIMULATION; MAPS; RANDOM PROCESSES; SET THEORY; LAKES;

GLOBAL ANALYSIS; LOCAL DEPENDENCIES; ONOMASTIC DATA; SPATIAL REGIONS; COMPUTATIONAL TECHNIQUE; LINGUISTIC INFORMATION; NAMING SYSTEMS; NATURAL FEATURES; PROBABILISTIC MODELING; SPATIAL ASSOCIATION RULES;

LINGUISTICS; DATABASE SYSTEMS;

EID: 9444245074     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-39804-2_27     Document Type: Conference Paper

References (15)

1. Ripley, B.D.: Spatial Statistics. John Wiley & Sons (1981)
2. Bailey, T.C., Gatrell, A.C.: Interactive Spatial Data Analysis. Longman Scientific & Technical (1995)
3. Leskinen, T.: The geographic names register of the National Land Survey of Finland. In: Eighth United Nations Conference on the Standardization of Geographical Names. (2002)
4. Ripley, B.D.: The second-order analysis of stationary point processes. Journal of Applied Probability 13 (1976) 255-266
5. Koperski, K., Han, J.: Discovery of spatial association rules in geographic information databases. In: Proceedings of the 4th International Symposium on Large Spatial Databases. (1995)
6. Koperski, K.: A Progressive Refinement Approach to Spatial Data Mining. PhD thesis, Simon Praser University (1999)
7. Estivill-Castro, V., Lee, I.: Data mining techniques for autonomous exploration of large volumes of geo-referenced crime data. In: 6th International Conference on Geocomputation. (2001)
8. Huang, Y., Shekhar, S., Xiong, H.: Discovering co-location patterns from spatial datasets: A general approach. Submitted to IEEE Transactions on Knowledge and Data Engineering (TKDE), under second round review (2002)
9. Huang, Y., Xiong, H., Shekhar, S., Pei, J.: Mining confident co-location rules without a support threshold. To appear in Proceedings of the 18th ACM Symposium on Applied Computing (ACM SAC) (2003)
10. McLachlan, G., Peel, D.: Finite Mixture Models. Wiley Series in Probability and Statistics. John Wiley & Sons (2000)
11. Everitt, B., Hand, D.: Finite Mixture Distributions. Monographs on Applied Probability and Statistics. Chapman and Hall (1981)
12. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39 (1977) 1-38
13. Redner, R., Walker, H.: Mixture densities, maximum likelihood and the EM algorithm, SIAM Review 26 (1984) 195-234
14. McLachlan, G.J.: The EM Algorithm and Extensions. Wiley & Sons (1996)
15. Wu, C.J.: On the convergence properties of the EM algorithm. The Annals of Statistics 11 (1983) 95-103