Discovery Multiple Data Structures in Big Data through Global Optimization and Clustering Methods

In this paper, we propose an approach to Big Data visualization, based on clustering techniques, in order to find a structure of them and to facilitate their visualization. However, the main problem of clustering is that sometimes converge to a local minimum showing only one solution, so an optimization of the K-means algorithm has been proposed with the aim to escape from local minimum and to visualize different solutions of the same problem. In particular, we use the K-means algorithm with multiple random starting points, in order to find several solutions to the same problem. This algorithm considers the data of the Italian calls for tenders, extracted through a crawling technique, and optimized through the proposed approach to obtain multiple solutions. These are used to achieve a repository of products that can be easily displayed and inquired during the formulation of an offer from a bidder company willing to participate to a call for tenders. The case study results show the feasibility and validity of the proposed approach.