Study of a mixed similarity measure for classification and clustering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1574, Issue , 1999, Pages 375-379

  Ho, Tu Baa ^a   Nguyen, Ngoc Binh ^a   Morita, Takafumi ^a  

^a JAPAN ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DECISION TREES;

CLASSIFICATION AND CLUSTERING; COMPARATIVE STUDIES; DECISION TREE INDUCTION; DISCRETIZATIONS; META-KNOWLEDGE; SIMILARITY MEASURE; SUPERVISED CLASSIFICATION; UNSUPERVISED CLUSTERING;

K-MEANS CLUSTERING;

EID: 84947776553     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48912-6_51     Document Type: Conference Paper

References (8)

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3. Jain, A.K. and Dubes, R.C., Algorithms for Clustering Data Prentice Hall, Englewood Cliffs, 1988.
4. Lancaster, H.O., "The Combining of Probabilities Arising from Data in Discrete Distributions" in Biometrika, Vol. 36, pp. 370-382, 1949.
5. Li, C. and Biswas, G., "Unsupervised Clustering with Mixed Numeric and Nominal Data - A New Similarity Based Agglomerative System" ADD: Techniques and Application, World Scientific, pp. 33-48, 1997.
6. MacQueen, J.B., "Some Methods for Classification and Analysis of Multivariate Observations", Proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297, 1967.
7. Quinlan, J.R. C4. 5: Programs for Machine Learning, Morgan Kaufmann, 1993.
8. Ralambondrainy, H., "A Conceptual Version of the k-means Algorithms", Pattern Recognition Letters, Vol. 16, pp. 1147-1157, 1995.