Data Mining in Education: Discussing Knowledge Discovery in Database (KDD) with Cluster Associative Study

Data Mining, being an emerging field has ties with ever-increasing knowledge base, contributing to historical data trends and patterns as well as providing evidence to predict future behaviors. With the adoption of data mining techniques and their thorough implications, the knowledge base could be enhanced and a futuristic strategic management tool could be formulated in the form of modern data mining techniques. The technique and terminology used in the paper involves extracting knowledge from large datasets. In this paper, cluster sampling associated with data mining is used to dig out a beneficial way of tackling data mining steps. With these combined data mining efforts, the aim of highlighting the significance of data mining could be amalgamated with data warehouses and the steps that need to involve selecting, transforming, mining, and output evaluation to get beneficial or desired results. Furthermore, a mild emphasize is put on the cluster sampling data mining technique, steps, and its implication to enhance knowledge, explore new terms, and broaden our output scenarios so that there is a better understanding of how to move along the data mining channel. The objective of this study is to explain the basics of data mining and provide a way to further this research over data mining with cluster sampling and Knowledge discovery is data bases (KDD). For the said purpose, two clusters of the population of China are used in this study. One of which includes the percentage of students enrolled in the primary schools; and the other includes percentage of students enrolled in secondary schools in China. The steps of cluster sampling technique are followed to give a better understanding of the data mining approach.