Abstract
Currently, Big Data is one of the common scenario which cannot be avoided. The presence of the voluminous amount of unstructured and semi-structured data would take too much time and cost too much money to load into a relational database for analysis purpose. Beside that, regression models are well known and widely used as one of the important categories of models in system modeling. This chapter shows an extended version of fuzzy regression concept in order to handle real-time data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-based Fuzzy C-Means (GAFCM) and a convex hull-based regression approach, which is regarded as a potential solution to the formation of information granules. It is shown that a setting of Granular Computing with the proposed approach, helps to reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. Additionally, the proposed approach shows an efficient real-time processing of information granules regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design sub-convex hulls as well as a main convex hull structure. In the proposed design setting, it was emphasized a pivotal role of the convex hull approach or more specifically the Beneath-Beyond algorithm, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.
Published Version
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