A Hybrid Clustering Model for Determining Financially Needy Students for Sponsorship

Many organizations in the world are seeking effective means to provide financial assistance brilliant but needy students. research works have been conducted determine students based on classification methods which fail separate financially stable from ones as class labels for affected by manual reviews, randomness and discretion of selection committee. This paper presents a hybrid clustering method determining income expenditure using K-Means Expectation-Maximization with logical AND operator eliminate limitations associated methods. The study reveals that hybridizing yield better results. Also, normalized data categorization, Euclidean distance gives results than Manhattan algorithm.