Correlation Analysis of Stock Index Data Features Using Sequential Rule Mining Algorithms

1. Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceedings of the international conference on data engineering (ICDE’95), Taipei, Taiwan, pp 3–14

   Google Scholar 

2. Mooney CH, Roddick JF (2013) Sequential pattern mining—approaches and algorithms. ACM N(M 20YY):1–46

   Google Scholar 

3. Motegaonkar VS et al (2014) A survey on sequential pattern mining algorithms. Int J Comput Sci Inform Technol 5(2)

   Google Scholar 

4. Kour A (2017) Sequential rule mining, methods and techniques: a review. Int J Comput Intell Res 13. ISSN 0973-1873

   Google Scholar 

5. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB conference, Santiago, Chile

   Google Scholar 

6. NSE online sources as on 10th May (2022). https://www.nseindia.com/products-services/indices-nifty50-index

7. National stock exchange—Nifty 50 historical data downloading online sources, updated as on 10th May 2022. https://www1.nseindia.com/products/content/equities/indices/historical_index_data.htm

8. Bustos O, Pomares-Quimbaya A (2020) Stock market movement forecast: a systematic review. Exp Syst Appl 156:113464

   Article  Google Scholar 

9. Drakopoulou V (2015) A review of fundamental and technical stock analysis techniques. J Stock Forex Trad. https://doi.org/10.4172/2168-9458.1000163

   Article  Google Scholar 

10. Liu H, Huang S, Wang P, Li Z (2021) A review of data mining methods in financial markets. Data Sci Finan Econ DSFE 1(4):362–392. https://doi.org/10.3934/DSFE.2021020

11. Barboza F, Kimura H, Altman E (2017) Machine learning models and bankruptcy prediction. Exp Syst Appl 83:405–417

    Article  Google Scholar 

12. Lee TK, Cho JH, Kwon DS et al (2019) Global stock market investment strategies based on financial network indicators using machine learning techniques. Exp Syst Appl 117:228–242

    Article  Google Scholar 

13. Rouf N et al (2021) Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. MDPI. https://doi.org/10.3390/electronics10212717

    Article  Google Scholar 

14. Aljawazneh H, Mora AM, Garcia-Sanchez P et al (2021) Comparing the performance of deep learning methods to predict companies’ financial failure. IEEE Access 9:97010–97038

    Article  Google Scholar 

15. Javed Awan M, Mohd Rahim MS, Nobanee H et al (2021) Social media and stock market prediction: a big data approach. Comput Mater Con 67:2569–2583

    Google Scholar 

16. Farid S, Tashfeen R, Mohsan T et al (2020) Forecasting stock prices using a data mining method: evidence from emerging market. Int J Finan Econ

    Google Scholar 

17. Kumar DA, Murugan S (2013) Performance analysis of Indian stock market index using neural network time series model. In: 2013 international conference on pattern recognition, informatics and mobile engineering. IEEE

    Google Scholar 

18. Ting J, Fu T, Chung F (2018) Mining of stock data: intra- and inter-stock pattern associative classification

    Google Scholar 

19. Online source: https://t4tutorials.com/support-confidence-minimum-support-frequent-itemset-in-data-mining/

20. Online source: https://pages.di.unipi.it/pibiri/papers/phd_thesis.pdf

21. Online source: https://www.baeldung.com/cs/hash-table-vs-trie-prefix-tree

22. Online source: http://cslibrary.stanford.edu/110/BinaryTrees.html

23. Online sources: https://www.geeksforgeeks.org/hashing-set2-separate-chaining/amp/ https://runestone.academy/ns/books/published/pythonds/SortSearch/Hashing.html

24. Biswas S, Chaki S, Mahbub K, Ahmed S (2021) Stock market prediction: a survey and evaluation. Conference Paper, ResearchGate. https://www.researchgate.net/publication/357205022

25. Nissim N, Lapidot Y, Cohen A, Elovici Y (2018) Trusted system calls analysis methodology aimed at detection of compromised virtual machines using sequential mining. Knowl-Based Syst 153:147–175

    Google Scholar 

26. Husak M, Bajtos T, Kaspar J, Bou-Harb E, Celeda P (2020) Predictive cyber situational awareness and personalized blacklisting: a sequential rule mining approach. ACM Trans Manage Inform Syst 11(4):1–16. Article No. 19

    Google Scholar 

27. Zheng L, Guo N, Chen W, Yu J, Jiang D (2020) Sentiment-guided sequential recommendation. In: SIGIR’20: proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, July-2020, pp 1957–1960. https://doi.org/10.1145/3397271.3401330

28. Tarus JK, Niu Z, Kalui D (2018) A hybrid recommender system for e-learning based on context awareness and sequential pattern mining. Soft Comput 22:2449–2461

    Google Scholar 

29. Anwar T, Uma V (2019) CD-SPM: cross-domain book recommendation using sequential pattern mining and rule mining. J King Saud Univ Comput Inform Syst

    Google Scholar 

30. Ibrahim R, Shafiq MO (2019) Detecting taxi movements using random swap clustering and sequential pattern mining. J Big Data. Article-39

    Google Scholar 

31. Bermingham L, Lee I (2020) Mining distinct and contiguous sequential patterns from large vehicle trajectories. Knowl-Based Syst 189

    Google Scholar 

32. Vu HQ et al (2017) Travel diaries analysis for sequential rule mining. J Travel Res 57(3):399–413

    Google Scholar 

33. Rjeily CB, Badr G, El Hassani AH, Andres E (2018) Medical data mining for heart diseases and the future of sequential mining. Mach Learn Paradigms 149:71–99

    Google Scholar 

34. Kaur I, Doja MN, Ahmad T (2020) Time-range based sequential mining for survival prediction in prostate cancer. J Biomed Inform 110

    Google Scholar 

35. Choi J, Jeong B, Yoon J (2019) Technology opportunity discovery under the dynamic change of focus technology fields: application of sequential pattern mining to patent classification. Tech Forecasting Soc Change 148

    Google Scholar 

36. Lee G, Kim D, Lee C (2020) A sequential pattern mining approach to identifying potential areas for business diversification. Asian J Technol Innov 28(I)

    Google Scholar 

37. Zhang L, Yang G, Li X (2020) Mining sequential patterns of PM2.5 pollution between 338 cities in China. J Environ Manage 262

    Google Scholar 

38. Srivastava G, Lin JC-W, Zhang X, Li Y (2020) Large-scale high-utility sequential pattern analytics in internet of things. Internet Things 8(16)

    Google Scholar 

39. Yao L, Huang H, Chen S-H (2020) Product quality detection through manufacturing process based on sequential pattern considering deep semantic learning and process rules. In: Fault detection and process diagnostics by using big data analytics in industrial applications, June-2020. MDPI

    Google Scholar 

40. Pushpalatha K, Ananthanarayana VS (2018) Multimedia document mining using sequential multimedia feature patterns. ResearchGate, https://arxiv.org/pdf/1808.01038

41. Spreafico R, Soriaga LB (2020) Advances in Genomics for drug development. MDPI. https://www.mdpi.com/2073-4425/11/8/942/pdf

42. Levi M, Hazan I (2020) Deep learning based sequential mining for user authentication in web applications. In: International workshop on emerging technologies for authorization and authentication, ETAA-2020. Springer, pp 1–15

    Google Scholar 

43. Estiri H, Strasser ZH et al (2020) Transitive sequencing of medical records for mining predictive and interpretable temporal representations. Patterns 1(4)

    Google Scholar 

44. Valdez F, Castilli O, Melin P (2021) Bio-inspired algorithms and its applications for optimization fuzzy clustering. MDPI. https://doi.org/10.3390/a14040122

    Article  Google Scholar 

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