Knowledge Discovery with Clustering: Impact of metrics and reporting phase by using KLASS

Title:

Knowledge Discovery with Clustering: Impact of metrics and reporting phase by using KLASS

Creator:

Velarde, J. M., Gibert, Karina, Nonell, R., and Colillas , M. M.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:4875f6cc-78fb-46e3-ae34-05d13fcabe0f
uuid:4875f6cc-78fb-46e3-ae34-05d13fcabe0f

Subject:

Ill.structured domains, heterogeneous data matrices, clustering, mixed metrics, class-interpretation, and software

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

One of the features involved in clustering is the evaluation of distances between individuals. This paper is related with the use of different mixed metrics for clustering messy data. Indeed, in real complex domains it becomes natural to deal with both numerical and symbolic attributes. This can be treated on different approaches. Here, the use of mixed metrics is followed. In the paper, impact of metrics on final classes is studied. The application relates to clustering municipalities of the metropolitan area of Barcelona on the bases of their constructive behavior, the number of buildings of different types being constructed, or the politics orientation of the local government. Importance of the reporting phase is also faced in this work. Both clustering with several distances and the interpretation oriented tools are provided by a software specially designed to support Knowledge Discovery on real complex domains, called KLASS.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2005 Volume:15 | Number:4

Harvested from:

CDK

Metadata only:

false