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首页> 外文期刊>Scientific programming >Analysis of Simple K- Mean and Parallel K- Mean Clustering for Software Products and Organizational Performance Using Education Sector Dataset
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Analysis of Simple K- Mean and Parallel K- Mean Clustering for Software Products and Organizational Performance Using Education Sector Dataset

机译：用教育部门数据集分析软件产品和组织绩效的简单K-均值和平行k-均值聚类

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Context . Educational Data Mining (EDM) is a new and emerging research area. Data mining techniques are used in the educational field in order to extract useful information on employee or student progress behaviors. 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. Objective . Data are the most valuable commodity for any organization. It is very difficult to extract useful information from such a large and massive collection of data. Data mining techniques are used to forecast and evaluate academic performance of students based on their academic record and participation in the forum. 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. Methodology . The current study sought to weigh up factors that contribute to improving student academic performance in Pakistan. In this paper, both the simple and parallel clustering techniques are implemented and analyzed to point out their best features. 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. Results . Both the algorithms are tested and compared with each other for a dataset of 10,000 and 5000 integer data items. The datasets are evaluated 10 times for minimum elapse time-varying K value from 1 to 10. The proposed study is more useful for scientific research data sorting. Scientific research data statistics are more accurate.
机译：语境 。教育数据挖掘（EDM）是一个新的和新兴的研究区。数据挖掘技术用于教育领域，以提取有关员工或学生进度行为的有用信息。学习数据的可用性的最新增加给了教育数据挖掘的重要性和势头，以更好地理解并优化它发生的学习过程和环境。客观的 。数据是任何组织最有价值的商品。从如此大而大规模的数据收集，很难提取有用的信息。数据挖掘技术用于根据学术记录和参与论坛的学生评估学生的学术表现。虽然已经进行了几项研究来评估全世界学生的学术表现，但缺乏适当的研究来评估能够提高学生的学术表现的因素。方法 。目前的研究旨在权衡有助于提高巴基斯坦学生学业成绩的因素。在本文中，实现并分析了简单和并行聚类技术以指出其最佳功能。并行K-均值算法克服了简单算法的问题，并行算法的结果始终相同，这提高了群集质量，迭代次数和经过时间。结果 。两个算法都经过测试，并将其彼此进行比较，用于10,000和5000个整数数据项的数据集。评估数据集10次，以便从1到10中的最小流逝时变k值。拟议的研究对于科研数据分类更有用。科学研究数据统计数据更加准确。

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