Paper Info
Title
Knowledge Discovery: Data Mining by Self-organizing Maps.
Abstract
Due to the characteristics offered by automated management systems, municipal administrations are now attempting to store digital information instead of keeping their physical documents. One consequence of such fact is the generation of large volume of data. Usually, these data are collected by ICT technologies and then stored in transactional databases. In this environment, collected data might have complex internal relationships. This may be an issue to identify patterns and behaviors. Many institutions use data mining techniques for recognize hidden patterns and behaviors in their operational data. These patterns can assist to future activities planning and provide better management to financial resources. Intelligent analysis can be realized using the Support Tools and Support Decision Making (STSDM). These tools can analyze large volume of data through previously established rules. These rules are presented for STSDM in the training phase, and the tool learns about the patterns that should look. This paper proposes a model to support decision making based on self-organized maps. This model, applied to electronic government tools, can recognize patterns in large volume of data without the set of rules for training. To perform our case study, we use data provided by the city of Campinas, Sao Paulo.
Year
DOI
Venue
2012
10.1007/978-3-642-36608-6_12
Lecture Notes in Business Information Processing
Keywords
Field
DocType
Business intelligence,Data mining,e-Government,Self-organizing maps
Data mining,E-Government,Computer science,Self-organizing map,Knowledge extraction,Information and Communications Technology,Business intelligence,Management system,Transactional leadership,Government
Conference
Volume
ISSN
Citations 
140
1865-1348
0
PageRank 
References 
Authors
0.34
4
4
Name
Order
Citations
PageRank
Everton Luiz de Almeida Gago Júnior100.68
Gean Davis Breda282.70
Eduardo Zanoni Marques311.42
Leonardo de Souza Mendes45311.44