Pattern-based clustering using unsupervised decision trees
In clustering, providing an explanation of the results is an important task. Pattern-based clustering algorithms provide, in addition to the list of objects belonging to each cluster, an explanation of the results in terms of a set of patterns that describe the objects grouped in each cluster. It ma...
| Author: | |
|---|---|
| Format: | doctoral thesis |
| Status: | Published version |
| Publication Date: | 2015 |
| Country: | México |
| Institution: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repository: | Repositorio Institucional del INAOE |
| Language: | English |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/29 |
| Online Access: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/29 |
| Access Level: | Open access |
| Keyword: | info:eu-repo/classification/Reconcimiento de patrones/Patter mining info:eu-repo/classification/Agrupación de patrones/Pattern-based clustering info:eu-repo/classification/Agrupación/Clustering info:eu-repo/classification/Datos mixtos/Mixed Datasets info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/330405 |
| Summary: | In clustering, providing an explanation of the results is an important task. Pattern-based clustering algorithms provide, in addition to the list of objects belonging to each cluster, an explanation of the results in terms of a set of patterns that describe the objects grouped in each cluster. It makes these algorithms very attractive from the practical point of view; however, patternbased clustering algorithms commonly have a high computational cost in the clustering stage. Moreover, the most recent algorithms proposed within this approach, extract patterns from numerical datasets by applying an a priori discretization process, which may cause information loss. In this thesis, we propose new algorithms for extracting only a subset of patterns useful for clustering, from a collection of diverse unsupervised decision trees induced from a dataset. Additionally, we propose a new clustering algorithm based on these patterns. |
|---|