Association mining-based method for enterprise’s technological innovation intelligent decision making under big data

Authors

  • Qianqian Zhang School of Information, Beijing Wuzi University, China
  • Guining Geng 360 Digital Security Technology Group Co., Ltd.
  • Qun Tu School of Economics and Management, Beijing University of Chemical Technology, China

DOI:

https://doi.org/10.15837/ijccc.2023.2.5241

Keywords:

Intelligent decision, association rule mining, enterprises technological innovation, FP-Growth algorithm

Abstract

Technological innovation is vital for the survival and development of enterprises. In the era of intelligent information interconnection and knowledge-driven economy, there is a growing interest in how to manage high-volume data, unlock its potential value, and provide intelligent analysis and decision-making support for enterprise’s technological innovation. This paper proposes an improved knowledge association analysis method based on the semantic concept model. This approach enables the discovery of potential correlations and interaction modes between the influencing factors of enterprise’s technological innovation, and provides a useful reference for decision-making by combining the analysis with the enterprise’s own situation.

References

Agarwal, R.(2017). Decision making with association rule mining and clustering in supply chains International Journal of Data and Network Science, 1(1),11-18,2017.

https://doi.org/10.5267/j.ijdns.2017.1.003

Agarwal, R.; Pareek, S.; Sarkar, B.; Mittal, M. (2018). Ordering policy using multi-level association rule mining International Journal of Information Systems and Supply Chain Management, 11(4),84-101,2018.

https://doi.org/10.4018/IJISSCM.2018100105

Alangari, N.;Alturki, R. (2020). Association rule mining in higher education: A case study of computer science students Smart Infrastructure and Applications: Foundations for Smarter Cities and Societies, 311-328,2020.

https://doi.org/10.1007/978-3-030-13705-2_13

Barton, D.L.(1992). capabilities and core rigidities: A paradox in managing new product development Strategic management journal, 13(S1),111-125,1992.

https://doi.org/10.1002/smj.4250131009

Bloch, C.(2007). Assessing recent developments in innovation measurement: the third edition of the Oslo Manual Science and Public Policy, 34(1),23-34,2007.

https://doi.org/10.3152/030234207X190487

Boone,T.;Ganeshan,R.;Jain, A.;Sanders, N.R.(2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting, 35(1),170-180,2019.

https://doi.org/10.1016/j.ijforecast.2018.09.003

Borah, A.; Nath, B.(2018). Identifying risk factors for adverse diseases using dynamic rare association rule mining, Expert systems with applications, 113,233-2632018,2018.

https://doi.org/10.1016/j.eswa.2018.07.010

Burgelman,R.A.;Christensen,C.M.;Wheelwright,S.C.(1998). Management of technology and innovation, McGraw-Hill/Irwin, 2008.

Cetindamar, D.;Shdifat, B.; Erfani,E.(2022). Understanding big data analytics capability and sustainable supply chains, Information Systems Management, 39(1),19-33,2022.

https://doi.org/10.1080/10580530.2021.1900464

Chen, J.;Adamson, C.(2015). Innovation: Integration of random variation and creative synthesis, Academy of Management Review, 40(3),461-464,2015.

https://doi.org/10.5465/amr.2014.0438

Das, S.;Sun, X.;Goel, S.;Sun, M.;Rahman, A.;Dutta, A.(2022). Flooding related traffic crashes: findings from association rules Journal of Transportation Safety & Security, 14(1),111-129,2022.

https://doi.org/10.1080/19439962.2020.1734130

Davidaviciene, V.;Markus, O.;Davidavicius, S.(2020). Identification of the opportunities to improve customer's experience in e-commerce, Journal of logistics, informatics and service science, 42-57,2020.

Diaconu, M.(2011). Technological Innovation: Concept, Process, Typology and Implications in the Economy, Theoretical & Applied Economics, 18(10),2011.

Du, G.;Liu, Z.;Lu, H.(2021). Application of innovative risk early warning mode under big data technology in Internet credit financial risk assessment, Journal of Computational and Applied Mathematics, 386,113260,2021.

https://doi.org/10.1016/j.cam.2020.113260

Ducange, P.; Pecori, R.; Mezzina, P.(2018). A glimpse on big data analytics in the framework of marketing strategies, Soft Computing, 22(1),325-342,2018.

https://doi.org/10.1007/s00500-017-2536-4

Erevelles, S.;Fukawa, N.;Swayne, L.(2016). Big Data consumer analytics and the transformation of marketing, Journal of business research, 69(2),897-904,2016.

https://doi.org/10.1016/j.jbusres.2015.07.001

Frishammar, J.; Florén, H.; Wincent, J.(2010). Beyond managing uncertainty: Insights from studying equivocality in the fuzzy front end of product and process innovation projects, IEEE Transactions on Engineering Management, 58(3),551-563,2010.

https://doi.org/10.1109/TEM.2010.2095017

Guleria, P.;Sood, M.(2020). Intelligent Data Analysis Using Hadoop Cluster-Inspired MapReduce Framework and Association Rule Mining on Educational Domain, Intelligent Data Analysis: From Data Gathering to Data Comprehension, 137-156,2020.

https://doi.org/10.1002/9781119544487.ch7

Guo, Y.;Wang, N.;Xu, Z.Y.;Wu, K. (2020). The internet of things-based decision support system for information processing in intelligent manufacturing using data mining technology, Mechanical Systems and Signal Processing, 142,106630,2020.

https://doi.org/10.1016/j.ymssp.2020.106630

Hartmann, P.M.; Zaki, M.; Feldmann, N.;Neely, A.(2016). Capturing value from big data - a taxonomy of data-driven business models used by start-up firms, International Journal of Operations

https://doi.org/10.1108/IJOPM-02-2014-0098

& Production Management, 36(10),1382-1406,2016.

https://doi.org/10.1108/IJOPM-02-2014-0098

Hall, L.A.;Bagchi-Sen, S.(2007). An analysis of firm-level innovation strategies in the US biotechnology industry, Technovation, 27(1-2), 4-14, 2007.

https://doi.org/10.1016/j.technovation.2006.07.001

Hosseinioun, P.; Shakeri, H.; Ghorbanirostam, G.(2016). Knowledge-Driven decision support system based on knowledge warehouse and data mining by improving apriori algorithm with fuzzy logic, International Journal of Computer and Information Engineering, 10(3),528-533,2016.

Issad, H.A.; Aoudjit, R.; Rodrigues, J.J.P.C.(2019). A comprehensive review of Data Mining techniques in smart agriculture, Engineering in Agriculture, Environment and Food, 12(4),511- 525,2019.

https://doi.org/10.1016/j.eaef.2019.11.003

Kamsu-Foguem, B.; Rigal, F.; Mauget, F.(2013). Mining association rules for the quality improvement of the production process, Expert systems with applications, 40(4),1034-1045,2013.

https://doi.org/10.1016/j.eswa.2012.08.039

Kao, H.A.; Hsieh, Y.S.; Chen,C.H.;Lee, J. (2017). Quality prediction modeling for multistage manufacturing based on classification and association rule mining MATEC Web of Conferences 123,00029,2017.

https://doi.org/10.1051/matecconf/201712300029

Kline, S.J.;Rosenberg, N.(2010). An overview of innovation, Studies on science and the innovation process: Selected works of Nathan Rosenberg, 173-203,2010.

https://doi.org/10.1142/9789814273596_0009

Kumar,T.S.(2020). Data mining based marketing decision support system using hybrid machine learning algorithm, Journal of Artificial Intelligence, 2(03),185-193,2020.

https://doi.org/10.36548//jaicn.2020.3.006

Lee, C.K.H.; Choy, K.L.;Ho, G. T.; Chin, K. S.; Law, K. M.; Tse, Y. K.(2013). A hybrid OLAPassociation rule mining based quality management system for extracting defect patterns in the garment industry, Expert Systems with Applications, 40(7),2435-2446,2013.

https://doi.org/10.1016/j.eswa.2012.10.057

Li, X.;Wang, Y.;Li, D.(2019). Medical data stream distribution pattern association rule mining algorithm based on density estimation IEEE Access, 7,141319-141329,2019.

https://doi.org/10.1109/ACCESS.2019.2943817

Li, C.; Chen, Y.; Shang, Y.(2022). A review of industrial big data for decision making in intelligent manufacturing, Engineering Science and Technology, 29,101021,2022.

https://doi.org/10.1016/j.jestch.2021.06.001

Li, J.; Pan, S.; Huang, L.(2019). A machine learning based method for customer behavior prediction, Tehnički vjesnik, 26(6),1670-1676,2019.

https://doi.org/10.17559/TV-20190603165825

Li, J.(2022). Venture financing risk assessment and risk control algorithm for small and mediumsized enterprises in the era of big data, Journal of Intelligent Systems, 31(1),611-622,2022.

https://doi.org/10.1515/jisys-2022-0047

Liu, P.;Wang, Q.;Liu, W.(2021). Enterprise human resource management platform based on FPGA and data mining, Microprocessors and Microsystems, 80, 103330, 2021.

https://doi.org/10.1016/j.micpro.2020.103330

Lin, H.;Sun, H.(2022). Short-Term Demand Forecasting Methods for Public Bicycles Under Big Data Environment, Innovative Computing: Proceedings of the 4th International Conference on Innovative Computing, 1039-1046,2022.

https://doi.org/10.1007/978-981-16-4258-6_127

Lokshin, B.;Van Gils, A.;Bauer, E.(2009). Crafting firm competencies to improve innovative performance, European Management Journal, 27(3),187-196,2009.

https://doi.org/10.1016/j.emj.2008.08.005

Ma, D.;Hu, J.;Yao, F.(2021). Big data empowering low-carbon smart tourism study on low-carbon tourism O2O supply chain considering consumer behaviors and corporate altruistic preferences, Computers & Industrial Engineering, 153,107061,2021.

https://doi.org/10.1016/j.cie.2020.107061

Maheshwari, S.;Gautam, P.;Jaggi, C. K.(2021). Role of Big Data Analytics in supply chain management: current trends and future perspectives, International Journal of Production Research, 59(6),1875-1900,2021.

https://doi.org/10.1080/00207543.2020.1793011

Mesbahi, N.;Kazar, O.; Benharzallah, S.;Zoubeidi, M.;Bourekkache, S.(2015). Multi-agents approach for data Mining based K-Means for improving the decision process in the ERP systems, International Journal of Decision Support System Technology, 7(2),1-14,2015.

https://doi.org/10.4018/IJDSST.2015040101

Nazifa, T.H.;Ramachandran, K.K.(2019). Information sharing in supply chain management: a case study between the cooperative partners in manufacturing industry, Journal of System and Management Sciences, 9(1),19-47,2019.

Ogbuke, N.J.; Yusuf, Y.Y.;Dharma, K.;Mercangoz, B.A.(2022). Big data supply chain analytics: ethical, privacy and security challenges posed to business, industries and society, Production Planning & Control, 33(2-3),123-137,2022.

https://doi.org/10.1080/09537287.2020.1810764

Orsic, J.;Rosi, B.;Jereb, B.(2019). Measuring sustainable performance among logistic service providers in supply chains, Tehnicki vjesnik, 26(5),1478-1485,2019.

https://doi.org/10.17559/TV-20180607112607

Paul, R.;Groza, T.;Hunter, J.;Zankl, A.(2014). Semantic interestingness measures for discovering association rules in the skeletal dysplasia domain, Journal of biomedical semantics, 5(1),1-13,2014.

https://doi.org/10.1186/2041-1480-5-8

Pietronudo,M.C.;Croidieu, G.;Schiavone, F.A.(2022). solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management, Technological Forecasting and Social Change, 182,121828,2022.

https://doi.org/10.1016/j.techfore.2022.121828

Salamai, A.;Saberi, M.;Hussain. O;Chang, E.(2018). Risk identification-based association rule mining for supply chain big data, Security, Privacy, and Anonymity in Computation, Communication, and Storage: 11th International Conference and Satellite Workshops, SpaCCS 2018, 219-228,2018.

https://doi.org/10.1007/978-3-030-05345-1_18

Schumpeter, J.A. (2013). Capitalism, socialism and democracy, routledge, 2013.

https://doi.org/10.4324/9780203202050

Seyedan, M.;Mafakheri, F.(2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities, Journal of Big Data, 7(1),1-22,2020.

https://doi.org/10.1186/s40537-020-00329-2

Shang, H.;Lu, D.;Zhou, Q.(2021). Early warning of enterprise finance risk of big data mining in internet of things based on fuzzy association rules, Neural Computing and Applications, 33,3901- 3909,2021.

https://doi.org/10.1007/s00521-020-05510-5

Shelke, R.R.;Dharaskar, R.V.;Thakare,V.M.(2017). Data mining for supermarket sale analysis using association rule, International Journal of Trend in Scientific Research and Development, 1(4),179-183,2017.

https://doi.org/10.31142/ijtsrd94

Shirazi, F.;Mohammadi, M.(2019). A big data analytics model for customer churn prediction in the retiree segment, International Journal of Information Management, 48,238-253,2019.

https://doi.org/10.1016/j.ijinfomgt.2018.10.005

Tiwari,S.;Wee, H.M.;Daryanto,Y.(2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries, Computers & Industrial Engineering, 115,319-330,2018.

https://doi.org/10.1016/j.cie.2017.11.017

Thakkar,R.G.;Kayasth,M.;Desai,H.(2014). Rule based and association rule mining on agriculture dataset, International Journal of Innovative Research in Computer and Communication Engineering, 2(11),6381-6384,2014.

Wang,G.J.;Jin,S.G.(2015). Design and development of intelligent logistics system based on data mining and association rules technology, dvanced Materials Research, 1078,392-396,2015.

https://doi.org/10.4028/www.scientific.net/AMR.1078.392

Yao,L.(2019). Financial accounting intelligence management of internet of things enterprises based on data mining algorithm, Journal of Intelligent & Fuzzy Systems, 37(5),5915-5923,2019.

https://doi.org/10.3233/JIFS-179173

Zhou, H.;Sun, G.;Fu, S.;Fan, X.;Jiang, W.;Hu, S.;Li,L.(2020). A distributed approach of big data mining for financial fraud detection in a supply chain, Comput Mater Continua, 64(2),1091- 1105,2020.

https://doi.org/10.32604/cmc.2020.09834

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Published

2023-04-03

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