Classification of Binary Vectors by Stochastic Complexity
Stochastic complexity is treated as a tool of classification, i.e., of inferring the number of classes, the class descriptions, and the class memberships for a given data set of binary vectors. The stochastic complexity is evaluated with respect to the family of statistical models defined by finite mixtures of multivariate Bernoulli distributions obtained by the principle of maximum entropy. It is shown that stochastic complexity is asymptotically related to the classification maximum likelihood estimate. The formulae for stochastic complexity have an interpretation as minimum code lengths for certain universal source codes for storing the binary data vectors and their assignments into the classes in a classification. There is also a decomposition of the classification uncertainty in a sum of an intraclass uncertainty, an interclass uncertainty, and a special parsimony term. It is shown that minimizing the stochastic complexity amounts to maximizing the information content of the classification. An algorithm of alternating minimization of stochastic complexity is given. We discuss the relation of the method to the AUTOCLASS system of Bayesian classification. The application of classification by stochastic complexity to an extensive data base of strains ofEnterobacteriaceaeis described.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 63 (1997)
Issue (Month): 1 (October)
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/supportfaq.cws_home/regional|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Gilles Celeux & Gérard Govaert, 1991. "Clustering criteria for discrete data and latent class models," Journal of Classification, Springer, vol. 8(2), pages 157-176, December.
- Mats Gyllenberg & Timo Koski, 1996. "Numerical taxonomy and the principle of maximum entropy," Journal of Classification, Springer, vol. 13(2), pages 213-229, September.
- Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer, vol. 50(2), pages 159-179, June.
- Michael Windham, 1987. "Parameter modification for clustering criteria," Journal of Classification, Springer, vol. 4(2), pages 191-214, September.
When requesting a correction, please mention this item's handle: RePEc:eee:jmvana:v:63:y:1997:i:1:p:47-72. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.