Treffer: Using a model of parallel distributed processing associated with data mining in the characterization of sexuality in a university population.
Title:
Using a model of parallel distributed processing associated with data mining in the characterization of sexuality in a university population.
Authors:
de Azevedo Simões PW; Computer Science Course, University of Extremo Sul Catarinense, Criciúma, SC, Brazil., Martins PJ, Casagrandre RA, Madeira K, de Mattos MC, Manenti SA, da Rosa MI, Dal-Pizzol F, Venson R, Coral LN, de Souza GS, Pandini JC, Cassettari Junior JM, Moretti GP, Cesconetto S
Source:
Studies in health technology and informatics [Stud Health Technol Inform] 2013; Vol. 192, pp. 1135.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform
Imprint Name(s):
Original Publication: Amsterdam ; Washington, DC : IOS Press, 1991-
MeSH Terms:
Computer Communication Networks* , Data Mining* , Programming Languages* , Software*, Sexual Behavior/*statistics & numerical data , Students/*statistics & numerical data , Universities/*statistics & numerical data, Algorithms ; Cluster Analysis ; Fuzzy Logic ; Humans ; Pattern Recognition, Automated/methods ; Population Dynamics
Entry Date(s):
Date Created: 20130808 Date Completed: 20150413 Latest Revision: 20130807
Update Code:
20250114
PMID:
23920909
Database:
MEDLINE
Weitere Informationen
Using the framework for developing parallel applications Java Parallel Programming Framework were conducted performance analysis of an application for the clustering data by the method of fuzzy logic combined with Gustafson-Kessel algorithm. In addition to running in a distributed environment, for comparative purposes, were also conducted collections of processing time in environments with a single Personal Computer approach. With the results obtained by collecting time of application, there was a statistical analysis to validate the application and the algorithm as well as the use of computational clustering as a way to increase performance applications.