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Top1. Introduction
Students’ academic performance analysis has become an imperative and challenging task for various universities and institutions to ensure the progressive growth of every student. In the conventional education system, academic performance had been monitored only at the end of every semester. The system was unable to give importance to steady and consistent growth of knowledge which is an essential component in education. The end-semester evaluation pattern failed to verify the subject knowledge of a student rather could generally confirm the memory power of the student. In order to overcome the limitation of conventional education system, continuous assessment has been introduced. Continuous evaluation pattern consists of various types of competency components such as presentation, assignment, case-study, quiz, open-book-test, group-discussion etc. along with the written examination. The continuous evaluation pattern facilitates to improve students’ performance in a consistent manner as well as assist to achieve in-depth knowledge in the respective subject (Shovon and Haque, 2012). Knowledge improvement in the specified set of subjects of a course contributes to better employability of the student.
Having large data sets of students’ performance data of continuous assessment, data mining technique has been found efficient to extract useful but hidden information is discussed by authors of Aggarwal and Yu (2000) and Kabakchieva (2013). The information if extracted can strengthen the confidence of students with good academic performance and improve their forthcoming performance to achieve better prospects. Also, students with poor performance get to improve in advance to meet the challenges and requirements of the job market.
Data mining techniques have been used with relational databases to discover unknown patterns, searching for unexpected results and correlations. Clustering which is considered to be the unsupervised learning technique, is one of the data mining technique which can accomplish the data mining task of identifying hidden patterns that exists in students’ performance without having any prior threshold knowledge as inputs. A subset of dimensions from the high dimensional data set with a subset of data points is said to be a subspace. Finding clusters in these subspaces is called subspace clustering. As similar to most of the real-world data sets which are high dimensional, the student data set considered for the study, having student performance data across different subjects of a course has been also identified to be of high dimensions. Therefore, discovering a quality and meaningful cluster from it becomes a challenging task is identified by authors of pawar (2002), Parsons, et al. (2004), Sembiring et al. (2007) and Woo et al. (2012). The aim of using subspace clustering algorithm in this work is to automatically identify subspaces of the feature space in which clusters exists. The subspace clustering algorithm helps to find good quality clusters in subspaces of high dimensional data without forcing the user to have domain knowledge about the clusters, scalability towards high dimensional data sets, non-presumption of any canonical data distribution, insensitive to the order of input data etc. This study has considered subspace clustering technique to identify hidden patterns, as it can help to analyze the students’ academic performance which includes various competency parameters to assess the interest and understanding of the students to meet the objectives of the course been offered.