Metrics for Association Rule Clustering Assessment
Loading...
Files
External sources
External sources
Date
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Type
Work presented at event
Access right
Acesso aberto
Files
External sources
External sources
Abstract
Issues related to association mining have received attention, especially the ones aiming to discover and facilitate the search for interesting patterns. A promising approach, in this context, is the application of clustering in the pre-processing step. In this paper, eleven metrics are proposed to provide an assessment procedure in order to support the evaluation of this kind of approach. To propose the metrics, a subjective evaluation was done. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Besides, the metrics do the users think about aspects related to the problems and provide a flexible way to solve them. Some experiments were done in order to present how the metrics can be used and their usefulness.
Description
Keywords
Association rules, Pre-processing, Clustering, Evaluation metrics
Language
English
Citation
Transactions On Large-scale Data- And Knowledge- Centered Systems Xvii. Berlin: Springer-verlag Berlin, v. 8970, p. 97-127, 2015.
