一种改进Apriori算法在移动平台教学评价中的应用研究

发布时间：2018-06-25 21:48

本文选题：关联规则 + Apriori算法　；参考：《重庆师范大学》2017年硕士论文

【摘要】：信息技术的飞速发展促使数据大量增加与积累,与此同时也促进了数据库技术的发展,当今数据挖掘技术已深入到社会中的各个领域。数据挖掘技术可以对数据进行异常检测、分类预测、关联规则挖掘、聚类分析等。其中关联规则技术能从大量的数据中挖掘频繁模式,根据这些规则分析出潜在的有价值的信息。教学评价是高校教学质量监督的一个重要手段,如今在各个高校已经得到了普及,但教学评价数据没有得到充分的利用,且评价方式单一。本文分别在数据挖掘以及关联规则中的经典算法Apriori进行了详细的介绍,描述算法的思想、实现过程以及算法所存在的不足。针对存在的缺陷,在分析前人改进的思想与方法,以及结合移动平台教学评价特征的基础上,对原经典Apriori算法进行了改进。改进后只需扫描一次原始数据库,对各个频繁项集所属事务进行存储,并将这些集合存储在新增的数据库中。因此,对自连接生成候选项集计数时,只需求候选项集的子集中存储的所属事务集合的交集,即可得到该候选项集的支持数。改进后的算法减少了对原始数据库的扫描次数,压缩了扫描的范围,从而提高了算法的效率。现今智能手机已成为我们日常生活的重要部分,为顺应时代发展,设计一种基于移动平台的教学评价系统成为了需求。在开展了高校师生对教学评价系统的需求调查后,对基于移动平台的教学评价系统进行需求分析和设计,并在Android平台上进行实现,完善教学评价系统的评价模式。基于移动平台的教学评价系统一方面满足了全校师生的需求,提供极大的便利,因此也能大大的提高参评率,使得数据更加全面、完整。本研究在基于移动平台的教学评价环境的基础上,将收集到的教学评价数据进行预处理,利用改进后的Apriori算法对评价数据进行挖掘。通过对挖掘结果进行分析和总结,得到潜在于数据背后的信息,这些重要信息将成为提高高校教学质量的重要依据。
[Abstract]:The rapid development of information technology promotes the increase and accumulation of data, at the same time, it also promotes the development of database technology. Nowadays, data mining technology has been deeply into every field of society. Data mining technology can be used for anomaly detection, classification prediction, association rule mining, clustering analysis and so on. Among them, association rules technology can mine frequent patterns from a large amount of data, and analyze potential valuable information according to these rules. Teaching evaluation is an important means of teaching quality supervision in colleges and universities. Nowadays, it has been popularized in various colleges and universities, but the data of teaching evaluation have not been fully utilized, and the evaluation method is single. In this paper, the classical algorithm Apriori in data mining and association rules is introduced in detail, and the idea, implementation process and shortcomings of the algorithm are described. In view of the defects, the classical Apriori algorithm is improved on the basis of analyzing the ideas and methods of the predecessors and combining the characteristics of the teaching evaluation of mobile platform. After the improvement, the original database is scanned only once, each frequent item set belongs to the transaction to store, and these collections are stored in the new database. Therefore, when the self-connected generation candidate set is counted, only the intersection of the set of transactions stored in the subset of the candidate item set is required, and the support number of the candidate set can be obtained. The improved algorithm reduces the number of scans on the original database and compresses the scanning range, thus improving the efficiency of the algorithm. Nowadays, smart phone has become an important part of our daily life. In order to adapt to the development of the times, it is necessary to design a teaching evaluation system based on mobile platform. After carrying out the investigation on the demand of the teaching evaluation system by the teachers and students in colleges and universities, the requirement analysis and design of the teaching evaluation system based on the mobile platform are carried out, and it is realized on the Android platform to perfect the evaluation mode of the teaching evaluation system. On the one hand, the teaching evaluation system based on mobile platform can meet the needs of the whole school teachers and students, provide great convenience, so it can also greatly improve the rate of participation, making the data more comprehensive and complete. On the basis of the teaching evaluation environment based on mobile platform, this study preprocesses the collected teaching evaluation data and uses the improved Apriori algorithm to mine the evaluation data. Through the analysis and summary of the mining results, the information underlying the data can be obtained. These important information will become an important basis for improving the teaching quality of colleges and universities.
【学位授予单位】：重庆师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP311.13;TP311.52

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