SMDICFBA: Software model for distributed incremental closeness factor based Algorithms

Abstract

The number of users utilizing internet services per day is in billions today. Also with the invent of “Internet of Everything (IoE)” and “Internet of People (IoP)”, the gigantic data is getting generated in real time every moment. To effectually handle, control, guide and utilize such vast amount of data in real time, it is essential to have distributed systems at place. Such distributed system for data management needs to be iterative in nature and parameter-free, so as to achieve quality decision making with prediction and or forecasting. “Distributed Incremental Closeness Factor Based Algorithm (DICFBA)” is primarily designed to accommodate ever growing data in numeric as well as text form. Assorted versions of CFBA are developed as per the need of the analysis till date. The primary purpose of all these CFBA driven incremental clustering models was to learn incrementally about embedded patterns from the given raw datasets. This research covers transformed CFBA models, its real-time varied domain applications, and future extensions for incremental classification point of view. Software development life cycle (SDLC) and software model for DICFBA (SMDICFBA) variants are also discussed in this chapter.