Clustering also called the unsupervised learning method involves grouping of data items into clusters that have high similarity but are dissimilar to data items of other clusters. This mechanism is a classification technique of data mining. After this comparison, a decision can be made. In order to understand a new phenomenon or to learn about a new object, people always try to compare it with other phenomena or objects based on similarities or dissimilarities. Clustering and classification are the most popular techniques of data mining for retrieving data from large datasets classification is known as supervised learning phenomena while that of clustering is the unsupervised learning phenomena.Ĭlassification is also called supervised learning technique. Different efficient and effective data mining methods are used to mine useful and important information from bulky datasets. These include transactional data also called operational data, i.e., cost, sales, accounting, payroll, and inventory, and nonoperational data, i.e., forecasting of data, industry sales and macroeconomic data, and metadata, such as data dictionary definitions or logical database design. Nowadays, business organizations are accumulating grooving and huge volumes of data in different setups, formats, and databases. Data are the collection of any facts, figures, and numbers that can be processed in order by a computer system.
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Such large datasets are growing rapidly so that it is a very challenging task to extract and mine important information, using conventional techniques. Every day, people come across a large amount of data and store these data for further exploration or analysis. Scientific research data statistics are more accurate. The proposed study is more useful for scientific research data sorting. The datasets are evaluated 10 times for minimum elapse time-varying K value from 1 to 10. Both the algorithms are tested and compared with each other for a dataset of 10,0 integer data items. The Parallel K-Mean algorithms overcome the problems of simple algorithm and the outcomes of the parallel algorithms are always the same, which improves the cluster quality, number of iterations, and elapsed time. In this paper, both the simple and parallel clustering techniques are implemented and analyzed to point out their best features. The current study sought to weigh up factors that contribute to improving student academic performance in Pakistan.
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Although several studies have been carried out to evaluate the academic performance of students worldwide, there is a lack of appropriate studies to assess factors that can boost the academic performance of students. Data mining techniques are used to forecast and evaluate academic performance of students based on their academic record and participation in the forum.
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It is very difficult to extract useful information from such a large and massive collection of data. Data are the most valuable commodity for any organization. Recent increase in the availability of learning data has given importance and momentum to educational data mining to better understand and optimize the learning process and the environments in which it takes place.
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Data mining techniques are used in the educational field in order to extract useful information on employee or student progress behaviors. Educational Data Mining (EDM) is a new and emerging research area.