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Research paper | Open Access

Identification of data mining research frontier based on conference papers

Yue Huang^¹(), Hu Liu^², Jing Pan^³

School of Information Science, Beijing Language and Culture University, Beijing, China

International Business School, Beijing Foreign Studies University, Beijing, China

School of Economics and Management, University of Science and Technology Beijing, Beijing, China

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Abstract

Purpose

Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers.

Design/methodology/approach

This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords.

Findings

Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich.

Originality/value

This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.

Keywords

Data mining Bibliometrics CiteSpace Conference papers Research frontier

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International Journal of Crowd Science

Volume 5 Issue 2,
August 2021

Pages 143-153

DOI: 10.1108/IJCS-01-2021-0001

Cite this article:

Huang Y, Liu H, Pan J. Identification of data mining research frontier based on conference papers. International Journal of Crowd Science, 2021, 5(2): 143-153. https://doi.org/10.1108/IJCS-01-2021-0001

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Table. 1Top nine conferences of Google Scholar metrics in the field of data mining

Rank	Publication	Abbreviation	h5-index
1	ACM SIGKDD International Conference on Knowledge discovery and data mining	KDD	73
2	ACM International Conference on Web Search and Data Mining	WSDM	54
3	IEEE International Conference on Data Mining	ICDM	38
4	SIAM International Conference on Data Mining	SDM	35
5	ACM Conference on Recommender Systems	RecSys	34
6	European Conference on Machine Learning and Knowledge Discovery in Databases	ECMLPKDD	31
7	International Conference on Artificial Intelligence and Statistics	AISTATS	31
8	IEEE International Conference on Big Data	ICBD	25
9	Pacific-Asia Conference on Knowledge Discovery and Data Mining	PAKDD	22

Table. 2Keywords term frequency statistics of conference papers (frequency 160 or above)

Frequency	Year	Keyword
2,644	2007	Data mining
1,040	2007	Algorithm
1,033	2007	Artificial intelligence
1,001	2007	Recommender system
573	2007	Information retrieval
429	2007	Data set
394	2010	Social networking (online)
368	2007	Collaborative filtering
368	2007	Learning system
365	2008	Website
364	2008	World wide web
319	2013	Big data
303	2007	Classification
298	2007	Optimization
290	2007	Social network
288	2007	Clustering algorithm
256	2008	Search engine
245	2008	Classification (of information)
245	2007	Learning algorithm
237	2008	Forecasting
208	2007	Clustering
189	2010	Space division multiple access
178	2010	Matrix factorization
173	2007	Bayesian network
169	2009	Factorization
169	2010	Matrix algebra
168	2008	Semantics
160	2007	Machine learning

Table. 3Keywords betweenness centrality statistics of conference papers (centrality 0.13 or above)

Centrality	Year	Keyword
1.02	2007	Recommender system
0.97	2008	World wide web
0.91	2007	Problem-solving
0.9	2007	User interface
0.88	2010	Social networking (online)
0.88	2007	Social network
0.87	2007	Visualization
0.87	2007	Information system
0.74	2007	Information retrieval
0.66	2007	Learning system
0.63	2007	Machine learning
0.62	2008	Search engine
0.62	2008	International conference
0.58	2007	Mining
0.44	2007	Learning algorithm
0.34	2007	Optimization
0.33	2007	Data mining
0.31	2007	Parameter estimation
0.28	2007	Clustering problem
0.27	2007	Clustering algorithm
0.25	2007	Artificial intelligence
0.25	2007	Bayesian network
0.23	2007	Inference engine
0.21	2008	Classification (of information)
0.21	2008	Flow of solid
0.17	2007	Cluster analysis
0.15	2007	Gene expression
0.13	2007	Algorithm
0.13	2007	Gene expression data

Table. 4Keywords clustering statistics of conference papers

No.	Scale	Mean year	Cluster name
0	17	2010	Data mining
1	16	2010	Recommender system
2	16	2008	Deep learning
3	16	2008	Administrative data processing
4	15	2008	Clustering
5	14	2009	Search engine
6	13	2007	Collaborative filtering
7	13	2009	Artificial intelligence
8	13	2009	Social network
9	13	2008	Semisupervised learning
10	11	2008	Classification
11	1	2008	Statistical method
12	1	2008	Database system
13	1	2008	Perceptron algorithm
14	1	2008	Association rule
15	1	2008	Pattern
16	1	2016	Word embedding
17	1	2008	Electric network analysis
18	1	2008	Knowledge-based system
19	1	2007	Security
20	1	2008	Database
21	1	2008	Speed up
22	1	2016	Big data analytics