Page 211 - Contributed Paper Session (CPS) - Volume 4

CPS2182 Lynne Billard et al.

3. Conclusion
We have introduced a new -regressions algorithm and shown that it
works better than does the traditional -means algorithm on interval valued
data. More details can be found in Liu (2016).

References
1. Billard, L. (2008). Sample covariance functions for complex quantitative
data. In: Proceedings World Congress, International Association of
Statistical Computing (eds. M. Mizuta and J. Nakano) Japanese Society of
Computational Statistics, Japan, p. 157-163.
2. Charles, C. (1977). Regression Typologique et Reconnaissance des
Formes. These de 3eme cycle, Universite de Paris, Dauphine.
3. Diday, E. (1973). The dynamic clusters method in nonhierarchical
clustering. International Journal of Computer and Information Sciences
2, 61-88.
4. Diday, E. (1987). Introduction a l’approche symbolique en analyse des
donnees. Premier Jouneles Symbolique-Numerique, CEREMADE,
Universite Paris - Dauphine, 21-56.
5. Diday E. and Simon, J. C. (1976). Clustering analysis. In: Digital Pattern
Recognition (ed. K. S. Fu). Springer, Berlin, 47-94.
6. Liu, F. (2016). Cluster analysis for symbolic interval data using linear
regression method. Doctoral Dissertation, University of Georgia.

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