Knowledge Discovery from Library Automation via Bibliomining using the Apriori Algorithm

Abstract

Today which is called as the digital age with the considerably developing information systems, the constant increase in the data amount being recorded has revealed the concept of big data. Obtaining the strategic information which is crucial for decision-makers especially in managerial terms is only possible through processing these big data with accurate techniques. Data mining techniques have frequently been used in recent years in order to reach meaningful and useful knowledge among data stacks. In this study, the Apriori Algorithm was used for the managers of university library information systems, which provide data-oriented service, to make investment decisions in the future effectively and create user profiles. Within the scope of the study, an application was performed on the basis of an information system comprising of real data of Erzincan Binali Yıldırım University Central Library. By means of association rules, which are one of the descriptive models of data mining, ten different association rules regarding the joint borrowing of publications were applied and results were obtained in the confidence intervals of 57.1% and 95.8%. In addition, information such as library inventory, member profile, and publication borrowing habits were obtained and evaluations were made in line with this information at the end of the study. 

Keywords

• Association analysis
• Apriori algorithm
• Bibliomining
• Library information system
• Data mining

References

1. [1] Sözen, E., Bardak, T., Peker, H., Bardak, S., “Analysis of Factors Effecting Furniture Selection Using Apriori Algorithm”, Journal of Advanced Technology Sciences, 6(3): 679-684, (2017).
2. [2] Bardak, S., Bardak, T., “Investigation of the Most Problems in Furniture Products With Data Mining”, Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences International Indexed and Refereed, 7(13): 285-292, (2020).
3. [3] Keleş, A. E., Kaya, M., “The Analysis of the Factors Affecting the Productivity in the Wall Construction of the Using Apriori Data Mining Method”, Academic Informatics Conference, 831-836, (2014).
4. [4] Anwar M.A., Ahmed S.S., Khan M.A.U., “Application of Apriori Algorithm on Examination Scores. In: Patnaik”, Advances in Machine Learning and Computational Intelligence, Springer, Singapore, (2021).
5. [5] Wang, C., Zheng, X., “Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint”, Evolutionary Intelligence, 13: 39–49, (2020).
6. [6] Takci, H., Sogukpinar, I., “Discovery of Library Users' Access Patterns”, Information World, 3(1): 12-26, (2002).
7. [7] Pu, H., Yang, C., “Enriching user‐oriented class associations for library classification schemes” The Electronic Library, 21(2): 130-141, (2003).
8. [8] Decker, R., Höppner, M., “Strategic planning and customer intelligence in academic libraries” Library Hi Tech, 24(4): 504-514, (2006).

9. [9] Ucan, O., “Data Mining Applications in Digital Libraries: The Example of Akdeniz University Central Library”, Master thesis, Akdeniz University, Institute of Social Sciences, Antalya, 1-3, (2010).
10. [10] Hajek, P., Stejskal, J., “Advances in Enviroment Computational Chemistry and Bioscience”, Wseas Publisher, Montreux, 339-344, (2012).
11. [11] Uppal, V., Chandwani, G., “An Empirical Study of Application of Data Mining Techniques in Library System”, International Journal of Computer Applications, 74(11): 42-46, (2013).
12. [12] Zhang, Q. S., Wang, X. Y., “Research of Personalized Information Service Based on Association Rules”, Advanced Materials Research, 760(1): 1800-1803, (2013).
13. [13] Tempelman-Kluit, N., Pearce, A., “Invoking the User from Data to Design” College and Research Libraries, 75(5): 616–640, (2014).
14. [14] Yi, K., Chen, T., Cong, G., "Library personalized recommendation service method based on improved association rules", Library Hi Tech, 36(3): 443-457, (2018).
15. [15] Wang, S., Xu, J., Feng, Y., Peng, M., Ma, K., "A Markov logic network method for reconstructing association rule-mining tasks in library book recommendation", Information Discovery and Delivery, (2021).
16. [16] Hand, D. J., “Data Mining: Statistics and More?”, The American Statistician, 52(2): 112-118, (1998).
17. [17] Ganesh, S., “Data Mining: Should It be Included in the ‘Statistics’ Cirriculum”, The 6th International Conference on Teaching Statistics, South Africa, 1-4, (2002).
18. [18] Nicholson, S., “The bibliomining process: Data Warehousing and Data Mining for Library Decision Making”, Information Technology and Libraries, 22(4): 146-156, (2003).
19. [19] Fayyad, U., Shapiro, G. P., Smyth, P., “Knowledge Discovery and Data Mining: Towards a unifying framework”, International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, 82-88, (1996).
20. [20]Schroeder, A. T., “Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support”, Journal of the American Society for Information Science, 48(9): 862-863, (1997).
21. [21] Han, J., Fu, Y., “Discovery of Multiple-Level Association Rules from Large Databases”, 21th International Conference on Very Large Data Bases, Zurich, 1-12, (1995).
22. [22] Larose, D. T., Larose, C. D., “Discovering knowledge in data: an introduction to data mining”, 2nd edition, A John Wiley & Sons Publisher, New Jersey, 249-255, (2004).
23. [23] Agrawal, R., Srikant, R., “Fast Algorithms For Mining Association Rules”, 20th International Conference on Very Large Data Bases, Santiago, 1-32, (1994).
24. [24]Agrawal R, Imielinski T, Swami A., “Mining association rules between sets of items in large databases”, In Proceeding: International Conference Management Data, (1993).
25. [25]Liu, H., Wang, B., “An association rule mining algorithm based on boolean matrix”, Data Science Journal, 6(9): 559-565, (2007).
26. [26]Han, J., Kamber, M., “Data Mining: Concepts & Techniques”, 3rd edition, Morgan Kauffmann Publishers, San Francisco, 155-182, (2006).