Discovering the research front of a specific topic remains a significant challenge for researchers in all scientific areas. Over the last decade, burst term detection (BTD) in text streams has become a useful technique for bibliometrics and science mapping. It has been argued that analytical methods based on BTD can indicate certain facets of a research front. To integrate BTD into the framework of traditional co-word analysis, association rule mining between keywords and burst terms (ARM-KB) is introduced to enhance traditional co-word analysis and present a new facet of the research front for a field of science. Based on ARM-KB, possible connections between keywords and burst terms are built, which can facilitate the exploration of a research front from a three-dimensional perspective, through co-word analysis, burst term clues, and association rules. In the case study, the research fronts of anticancer based on nanomedicine (ABN) are explored. Based on theoretical and empirical analyses, ARM-KB can be used as a valuable new technique or a supplement to traditional bibliometrics in the exploration of scientific frontiers.
ChenCM.CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology2006; 57(3): 359–377.
2.
WangXKabánA.A dynamic bibliometric model for identifying online communities. Data Mining and Knowledge Discovery2008; 16: 67–107.
3.
AraujoLMereloJJ.Automatic detection of trends in time-stamped sequences: An evolutionary approach. Soft Computing2010; 14(3): 211–227.
4.
TakahashiTTomiokaRYamanishiK.Discovering emerging topics in social streams via link-anomaly detection. IEEE Transactions on Knowledge and Data Engineering2014; 26(1): 120–130.
5.
CoboMJLopez-HerreraAGHerrera-ViedmaE. Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology2011; 62(7): 1382–1402.
6.
KleinbergJ.Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery2003; 7: 373–397.
7.
PriceDD.Networks of scientific papers. Science1965; 149: 510–515.
8.
SmallHGGriffithBC.The structure of scientific literatures: Identifying and graphing specialties. Science Studies1974; 4: 17–40.
9.
PerssonO.The intellectual base and research fronts of JASIS 1986–1990. Journal of the American Society for Information Science1994; 45(1): 31–38.
10.
MorrisSAYenGWuZAsnakeB.Timeline visualization of research fronts. Journal of the American Society for Information Science and Technology2003; 55(5): 413–422.
11.
ChenCMIbekwe-SanJuanFHouJ.The structure and dynamics of co-citation clusters: A multiple-perspective co-citation analysis. Journal of the American Society for Information Science and Technology2010; 61(7): 1386–1409.
12.
ChenCMLeydesdorffL.Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis. Journal of the Association for Information Science and Technology2014; 65(2): 334–351.
13.
NguyenTPhungDAdamsB. Event extraction using behaviors of sentiment signals and burst structure in social media. Knowledge and Information System2013; 37: 279–304.
14.
ChenCMDubinRKimMC.Emerging trends and new developments in regenerative medicine: A scientometric update (2000–2014). Expert Opinion on Biological Therapy2014; 14(9): 1295–1317.
15.
KimMCChenCM.A scientometric review of emerging trends and new developments in recommendation systems. Scientometrics2015; 104(1): 239–263.
16.
SunAZengDChenHC.Burst detection from multiple data streams: A network-based approach. IEEE Transactions on Systems, Man, and Cybernetics part C: Applications and Reviews2010; 40(3): 258–267.
17.
ChenWChundiP.Extracting hot spots of topics from time-stamped documents. Data & Knowledge Engineering2011; 70(7): 642–660.
18.
GuoHNWeingartSBoernerK.Mixed-indicators model for identifying emerging research areas. Scientometrics2011; 89(1): 421–435.
19.
ChenKHGuanJC.A bibliometric investigation of research performance in emerging nanobiopharmaceuticals. Journal of Informetrics2011; 5(2): 233–247.
CallonMCourtialJPLavilleF. Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics1991; 22(1): 153–203.
22.
LeeWH.How to identify emerging research fields using scientometrics: An example in the field of information security. Scientometrics2008; 76(3): 503–525.
23.
AnXYWuQQ.Co-word analysis of the trends in stem cells field based on subject heading weighting. Scientometrics 2011; 88(1): 133–144.
24.
Romo-FernándezLMGuerrero-BoteVPMoya-AnegónF.Co-word based thematic analysis of renewable energy (1990–2010). Scientometrics2013; 97: 743–765.
25.
ChoJ.Intellectual structure of the institutional repository field: A co-word analysis. Journal of Information Science2014; 40(3): 386–397.
26.
DingWYChenCM.Dynamic topic detection and tracking: A comparison of HDP, c-word, and co-citation methods. Journal of the American Society for Information Science and Technology2014; 65(10): 2084–2097.
27.
WuQQZhangCDHongQQChenLY.Topic evolution based on LDA and HMM and its application in stem cell research. Journal of Information Science2014; 40(5): 611–620.
28.
AgrawalRImielinskiTSwamiA. Mining association rules between sets of items in large database. In: Proceedings of the ACM SIGMOD Conference on Management of Data, Washington, DC, 1993, pp. 207–216.
29.
PasquierNBastideYTaouilR. Efficient mining of association rules using closed itemset lattices. Information Systems1999; 24(1): 25–46.
30.
SahooJDasAKGoswamiA.An efficient approach for mining association rules from high utility item sets. Expert Systems with Applications2015; 42(13): 5754–5778.
31.
LinW-YTsengM-C.Automated support specification for efficient mining of interesting association rules. Journal of Information Science2006; 32(3): 238–250.
32.
OrdonezCEzquerraNSantanaCA.Constraining and summarizing association rules in medical data. Knowledge and Information Systems2006; 9(3): 259–283.
33.
McNicholasPDMurphyTBO’ReganM.Standardising the lift of an association rule. Computational Statistics & Data Analysis2008; 52(10): 4712–4721.
34.
InokuchiAWashioTMotodaH.An apriori-based algorithm for mining frequent substructures from graph data. Lecture Notes in Computer Sciences2000; 1910: 13–23.
35.
GrahneGZhuJF.Fast algorithms for frequent itemset mining using FP-trees. IEEE Transactions on Knowledge and Data Engineering2005; 17(10): 1347–1362.
36.
AouadLMLe-KhacN-AKechadiTM.Performance study of distributed apriori-like frequent itemsets mining. Knowledge and Information Systems2010; 23(1): 55–72.
37.
NasongklaNBeyERenJM. Multifunctional polymeric micelles as cancer-targeted, MRI-ultrasensitive drug delivery systems. Nano Letters2006; 6(11): 2427–2430.
38.
ZhangZPLeeSHFengSS.Folate-decorated poly(lactide-co-glycolide)-vitamin E TPGS nanoparticles for targeted drug delivery. Biomaterials2007; 28(10): 1889–1899.
39.
ReddyLHDubernetCMouelhiSL. A new nanomedicine of gemcitabine displays enhanced anticancer activity in sensitive and resistant leukemia types. Journal of Controlled Release2007; 124(1–2): 20–27.
40.
PanJFengSS.Targeting and imaging cancer cells by Folate-decorated, quantum dots (QDs)-loaded nanoparticles of biodegradable polymers. Biomaterials2009; 30(6): 1176–1183.
41.
BhirdeAAPatelVGavardJ. Targeted killing of cancer cells in vivo and in vitro with egf-directed carbon nanotube-based drug delivery. ACS Nano2009; 3(2): 307–316.
42.
HuangWCChiangWHChengYH. Tumortropic monocyte-mediated delivery of echogenic polymer bubbles and therapeutic vesicles for chemotherapy of tumor hypoxia. Biomaterials2015; 71(12): 71–83.
43.
KamalyNFredmanGSubramanianM. Development and in vivo efficacy of targeted polymeric inflammation-resolving nanoparticles. Proceedings of the National Academy of Sciences of the United States of America2013; 110(16): 6506–6511.
44.
DaiHNLeeJSJinW. Targeted doxorubicin nanotherapy strongly suppressing growth of multidrug resistant tumor in mice. International Journal of Pharmaceutics2015; 495(1): 329–335.
45.
PorterALYoutieJShapiraP. Refining search terms for nanotechnology. Journal of Nanoparticle Research2008; 10: 715–728.
46.
RousseeuwPJ.Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics1987; 20: 53–65.
47.
NewmanMEJ. Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America2006; 103(23): 8557–8582.
48.
BhattacharyaSBasuPK.Mapping a research area at the micro level using co-word analysis. Scientometrics1998; 43(3): 359–372.
49.
KostoffRNScallerRR.Science and technology roadmaps. IEEE Transactions on Engineering Management2001; 48(2): 132–143.
50.
DingYChowdhuryGGFooS.Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing & Management2001; 37(6): 817–842.
51.
FengFZhangLYDuYN. Visualization and quantitative study in bibliographic databases: A case in the field of university–industry cooperation. Journal of Informetrics2015; 9(1): 118–134.
52.
LeydesdorffL.Why words and co-words cannot map the development of the sciences. Journal of the American Society for Information Science1997; 48(5): 418–427.
53.
SuH-NLeeP-C.Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in Technology Foresight, Scientometrics2010; 85(1): 65–79.
54.
ZupicICaterT.Bibliometric methods in management and organization. Organizational Research2015; 18(3): 429–472.
55.
LeeSParkYYoonWC.Burst analysis for automatic concept map creation with a single document. Expert Systems with Applications2015; 42(22): 8817–8829.
56.
WuQQZhangHBLanJ.K-state automaton burst detection model based on KOS: Emerging trends in cancer field. Journal of Information Science2015; 41(1): 16–26.
57.
HuangYSchuehleJPorterAL. A systematic method to create search strategies for emerging technologies based on the Web of Science: Illustrated for ‘Big Data’. Scientometrics2015; 105(3): 2005–2022.
58.
SunJSMaJLiuZY. Leveraging content and connections for scientific article recommendation in social computing contexts. Computer Journal2014; 57(9): 1331–1342.
59.
ZhangLGlanzelWYeFY.The dynamic evolution of core documents: An experimental study based on h-related literature (2005–2013). Scientometrics2016; 106(1): 369–381.
