Improved clustering algorithm in student's performance with high dimensional and large volume of educational data.

Data Mining and Analytics have transformed the way business is conducted. Prominent techniques of Data Mining include Clustering, Classification and Association Rule Mining. There are a lot of algorithms for cluster analysis. But all algorithms have some limitations especially for high dimensional dataset. In this paper modified the k-mean algorithm for better cluster accuracy and minimized the computational time processing the dataset for student academic performance. Students undergo immense stress and pressure to outscore their competitors. Higher and Technical Education has many challenges owing to voluminous data and various dimensions for acquisition and processing. Various Clustering Algorithms provide partition accuracy, computational time, lead time but lack visual assessment post partition. This paper proposes a variant of k-Means algorithm using comparative criteria of Partition accuracy (PA) parameter and computational time. [ABSTRACT FROM AUTHOR]