Singling out ill-fit items in a classification. Application to the taxonomy of "Enterobacteriaceae"

• Autorzy: Gyllenberg H. G. , Gyllenberg M. , Koski T. , Lund T. , Mannila H. , Meek C.
• Języki publikacji: EN

Abstrakty

EN

We address the problem of evaluating the quality of cluster assignment of individual items in a classification. The problem can also be viewed as outlier detection in classifications. We describe simple methods for this task based on the use of Naive Bayes classification. Applied to two existing classifications of 5313 strains of bacteria the method indicated that one classification is far more robust than the other. The observations that fit badly to their clusters are typically items whose classification is suspect also from other considerations. Removing these elements from the data set, performing clustering on the reduced data set, and adding the outliers back one-by-one yielded a clustering that has a higher likelihood than the previous accepted classifications. Investigation of this new clustering lead to suggested changes in the classification of 69 strains in the material.

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 1999
• Tom: Vol. 9, no. 1/2
• Strony: 97--105
• Opis fizyczny: Bibliogr. poz. 21

Twórcy

autor

Gyllenberg H. G.

autor

Gyllenberg M.

autor

Koski T.

autor

Lund T.

autor

Mannila H.

autor

Meek C.

• Instuitute of Biotechnology, University of Helsinki, Finland