图书情报知识 ›› 2016, Vol. 0 ›› Issue (5): 4-12.doi: 10.13366/j.dik.2016.05.004

• 专业教育 • 上一篇    下一篇

数据挖掘课程的知识体系构建

安璐,欧孟花,李纲   

Constructingthe Knowledge System of Data Mining Courses

摘要:

随着大数据时代的到来,数据挖掘逐渐成为图书馆学情报学专业的研究者与实践者需要掌握的一项重要技能,数据挖掘理所当然也成为该学科的重要课程。本文旨在从数据挖掘教材和硕博学位论文中识别数据挖掘领域的核心知识点,构建该课程的知识体系,从而促进其教学开展与质量提升。研究发现,数据挖掘领域由七大知识模块组成,包括基础理论、数据挖掘方法、平台与工具、支撑技术、复杂结构数据挖掘、数据挖掘应用、前沿和发展趋势。通过深入分析各知识模块的内容及其之间的关联,建立了较为完善的、系统的数据挖掘课程的知识体系。

关键词: 数据挖掘课程, 知识体系, 共词聚类, 教材, 硕博学位论文

Abstract:

With the advent of the big data era, data mining becomes an important technique for Library and Information Science researchers and practitioners. Thus, data mining also becomes an important course in this discipline. In this study, we identified core knowledge units from relevant text books and master and doctoral dissertations on data mining and constructed the knowledge system of the data mining course to promote teaching and improving quality. Results show that the field of data mining is composed of seven knowledge modules, including basic theory, data mining methods, platforms and tools, supporting technology, mining of complex structural data, data mining applications, and frontier and trends. We also discussed relationships among individual knowledge modules in detail to establish a comprehensive and systematic knowledge system of data mining courses.

Key words: Data mining course, Knowledge system, Co-word clustering textbooks, Master and doctoral dissertations