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| Author(s): |
Luiz Fernando Sommaggio Coletta
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2011-05-19 |
| Examining board members: |
Ricardo José Gabrielli Barreto Campello;
André Carlos Ponce de Leon Ferreira de Carvalho;
Alexandre Gonçalves Evsukoff
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| Advisor: | Eduardo Raul Hruschka |
| Abstract | |
Data mining techniques have played in important role in several areas of human kwnowledge. More recently, these techniques have found space in a new and complex setting in which the data to be mined are physically distributed. In this setting algorithms for data clustering can be used, such as some variants of the widely used Fuzzy C-Means (FCM) algorithm that support clustering data ditributed across different sites. Those methods have been studied under different names, like collaborative and parallel fuzzy clustring. In this study, we offer some augmentation of the two FCM-based clustering algorithms used to cluster distributed data by arriving at some constructive ways of determining essential parameters of the algorithms (including the number of clusters) and forming a set systematically structured guidelines as to a selection of the specific algorithm dependeing upon a nature of the data environment and the assumption being made about the number of clusters. A thorough complexity analysis including space, time, and communication aspects is reported. A series of detailed numeric experiments is used to illustrate the main ideas discussed in the study (AU) | |
| FAPESP's process: | 09/11268-9 - Collaborative Fuzzy Clustering |
| Grantee: | Luiz Fernando Sommaggio Coletta |
| Support Opportunities: | Scholarships in Brazil - Master |