ABSTRACT
This chapter explains educational data analysis using clustering and design of prominent clusters. In educational data mining, clustering can be used to group the students as per their activities. The idea behind constructing clusters based on the density properties of the database is derived from a human natural clustering approach. Hybrid hierarchical clustering techniques try to combine the best advantages of both agglomerative and divisive techniques. Cluster analysis is based on measuring similarity between objects by computing the distance between each pair. In K-means clustering technique, clusters are wholly dependent on the choice of the initial cluster centroids. A hierarchical clustering method consists into grouping data objects into a tree of clusters. K-means clustering algorithm is used to automatically cluster the students. This method is used to classify the students’ performance according to the learning style which is visual, active, and sequential. From a machine-learning perspective, clusters correspond to hidden patterns and search for clusters is categorized as unsupervised learning.
