You are required to pick one research article (having more than 5 pages) about a

Question

You are required to pick one research article (having more than 5 pages) about any topic falling under the domain of “Data mining and warehousing” and write Summary and Critique of that article (maximum two A4 pages in 12 size Times New Roman font).

A critique is not (only) a criticism. A critique is a specific style of essay in which you identify, evaluate, and respond to an author's ideas, both positively and negatively. To learn more about “How to write a critique”, explore this link:

http://www.uis.edu/ctl/wp-content/uploads/sites/76/2013/03/Howtocritiqueajournalarticle.pdf

You can choose any topic of your interest such as Clustering, OLAP, Classification, Visualization, or any relevant algorithm. Some sample research articles are given below:

Chaudhuri, Surajit, and Umeshwar Dayal. "An overview of data warehousing and OLAP technology." ACM Sigmod record 26.1 (1997): 65-74.

Basit, Hamid Abdul, and Stan Jarzabek. "A data mining approach for detecting higher-level clones in software." Software Engineering, IEEE Transactions on 35.4 (2009): 497-514. Ding, Shifei, et al. "Research on data stream clustering algorithms." Artificial Intelligence Review 43.4 (2015): 593-600.

Baradwaj, Brijesh Kumar, and Saurabh Pal. "Mining educational data to analyze students' performance." arXiv preprint arXiv:1201.3417 (2012).

Madria, Sanjay Kumar, et al. "Research issues in web data mining." DaWaK. 1999. March, Salvatore T., and Alan R. Hevner. "Integrated decision support systems: A data warehousing perspective." Decision Support Systems 43.3 (2007): 1031-1043.

Abdullah, Umair, Jamil Ahmad, and Aftab Ahmed. "Analysis of effectiveness of apriori algorithm in medical billing data mining." Emerging Technologies, 2008. ICET 2008. 4th International Conference on. IEEE, 2008.

Explanation / Answer

Analyze the student’s performance by mining educational data

            The main goal of data mining methodologies, to study student’s performance, Data mining provides many tasks that could be used to study the student performance. Normally the higher education institutions are to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering know ledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students,

            Data Mining can be used in educational fie ld to enhance our understanding of learning process to focus on identifying, extracting and evaluating variables related to the learning process of students

            The data collected from different applications require proper method of extracting knowledge from large repositories for better decision making. Knowledge discovery in databases (KDD), often called data mining, aims at the discovery of useful information from large collections of data.

            The main function’s of data mining is applying various methods and algorithms in order to discover and extract patterns of stored data. Data mining and knowledge discovery applications have got a rich focus due to its significance in decision making and it has become an essential component in various organizations.

            The discovered knowledge can be used for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students performance.

Data Mining Process

            In present day’s educational system, a student’s performance is determined by the internal assessment and end semester examination. The internal assessment is carried out by the teacher based upon students performance in educational activities such as class test, seminar, and assignments, general Proficient, attendance and lab work. The end semester examination is one that is scored by the student in semester examination. Each student has to get minimum marks to pass a semester in internal as well as end semester examination

Classification

            Classification is the most commonly applied data mining technique, which employs a set of pre classified examples to develop a model that can classify the population of records at large. This approach frequently employs decision tree or neural network based classification algorithms. The data classification process involves learning and classification. In Learning the training data are analyzed by classification algorithm. In classification test data are used to estimate the accuracy of the classification rules. If the accuracy is acceptable the rules can be applied to the new data tuples. The classifier training algorithm uses these pre classified examples to determine the set of parameters required for proper discrimination. The algorithm then encodes these parameters into a model called a classifier

Conclusion

            The classification task is used on student database to predict the students division on the basis of previous database. As there are many approaches that are used for data classification, the decision tree method is used here. Information’s like Attendance, Class test, Seminar and Assignment marks were collected from the student’s previous database, to predict the performance at the end of the semester.

This study will help to the students and the teachers to improve the division of the student. This study will also work to identify those students which needed special attention to reduce fail ration and taking appropriate action for the next semester examination.

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