GANY: A Genetic Spectral-based Clustering Algorithm for Large Data Analysis

Recently, Data analysis is one of the most growing fields. The big amounts of data are making their analysis a really challenging area. The most relevant techniques are mainly divided in two sub-domains: Classification and Clustering. Even though Classification is currently growing and evolving, one of the promising techniques to deal with the Large Data Analysis is Clustering, because Classification needs human supervision, which makes the analysis more expensive. Clustering is a blind process used to group data by similarity. Currently, the most relevant methods are those based on manifold identification. The main idea behind these techniques is to group data using the form they define in the space. In order to achieve this goal, there are several techniques based on Spectral Analysis which deal with this problem. However, these techniques are not suitable for Large Data, due to they require a lot of memory to determine the groups. Besides, there are some problems of local minima convergence in these techniques which are common in statistical methodologies. This work is focused on combining Genetic Algorithms with spectral-based methodologies to deal with the Large Data Analysis problem. Here, we will combine the Nystrom method with the Spectrum to generate an approximation of the problem to an accurate summary of the search space. Also a genetic algorithm is used to reduce the local minimum convergence problem in the new search space. The performance of this methodology has been evaluated using the accuracy with both, synthetic and real-world datasets extracted from the literature.