A Data Mining Approach to Identify Sexuality Patterns in a Brazilian University Population.

This paper presents the profile and experience of sexuality generated from a data mining classification task. We used a database about sexuality and gender violence performed on a university population in southern Brazil. The data mining task identified two relationships between the variables, which enabled the distinction of subgroups that better detail the profile and experience of sexuality.

Using a model of parallel distributed processing associated with data mining in the characterization of sexuality in a university population.

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.