Title
Exploring topics related to data mining on Wikipedia.
Abstract
Purpose - Data mining has been a popular research area in the past decades. Many researchers study data-mining theories, methods, applications and trends; however, there are very few studies on data-mining-related topics in social media. This paper aims to explore the topics related to data mining based on the data collected from Wikipedia. Design/methodology/approach - In total, 402 data-mining-related articles were obtained from Wikipedia. These articles were manually classified into several categories by the coding method. Each category formed an article-term matrix. These matrices were analysed and visualized by the self-organizing map approach. Several clusters were observed in each category. Finally, the topics of these clusters were extracted by content analysis. Findings - The articles obtained were classified into six categories: applications, foundation and concepts, methodologies, organizations, related fields and topics and technology support. Business, biology and security were the three prominent topics of the applications category. The technologies supporting data mining were software, systems, databases, programming languages and so forth. The general public was more interested in data-mining organizations than the researchers. They also focused on the applications of data mining in business more than in other fields. Originality/value - This study will help researchers gain insight into the general public's perceptions of data mining and discover the gap between the general public and themselves. It will assist researchers in finding new techniques and methods which will potentially provide them with new data-mining methods and research topics.
Year
DOI
Venue
2017
10.1108/EL-09-2016-0188
ELECTRONIC LIBRARY
Keywords
Field
DocType
Social media,Data mining,Social Web mining,Theme discovery
Data science,Data mining,World Wide Web,Content analysis,Web mining,Social media,Computer science,Coding (social sciences),Originality,Software
Journal
Volume
Issue
ISSN
35.0
SP4.0
0264-0473
Citations 
PageRank 
References 
0
0.34
19
Authors
2
Name
Order
Citations
PageRank
yanyan wang17721.79
Jin Zhang243.84