THE IMPORTANCE OF DATA MINING AND ITS USAGE IN LIBRARIES

Year 2023, , 503 - 541, 20.06.2023

Korcan Doğan , Sacit Arslantekin

https://doi.org/10.33171/dtcfjournal.2023.63.1.21

Abstract

Data mining is one of the most important methods to find patterns and draw meaningful conclusions from big data collected from different sources. It is important for libraries to collect data from different sources and to draw meaningful results from this data by data mining.
In this context, the main hypothesis in the study is: "It is possible for libraries to obtain new patterns in library operations and services by using data mining techniques and to use them to develop new service models by reflecting them in decision support processes."
A literature review is conducted to establish the theoretical basis of the research. At this stage, nationwide and international studies including the concepts related to data mining, data mining, data mining models, data mining processes, etc. are examined. The areas of utilization and applications of data mining are included in libraries.
As a result of the research, the findings and evaluations obtained through the data mining process are presented, and the main hypothesis and sub-hypotheses are confirmed.

Keywords

Data mining, Big data, Library services, Data mining models, Data mining processes

VERİ MADENCİLİĞİNİN ÖNEMİ VE KÜTÜPHANELERDE KULLANIMI

Abstract

Veri madenciliği farklı kaynaklardan toplanan büyük ölçekli verilerden örüntüler bulmak ve anlamlı sonuçlar çıkarabilmek için en önemli yöntemlerden biridir. Kütüphanelerin de farklı kaynaklardan veri toplayabilmesi ve bu verilerden veri madenciliği ile anlamlı sonuçlar çıkarabilmesi için önemlidir.
Bu noktadan hareketle çalışmada “Kütüphanelerin veri madenciliği tekniklerini kullanarak, işlem ve hizmetlerinde yeni örüntüler elde etmesi ve bunları karar destek süreçlerine yansıtarak yeni hizmet modelleri geliştirmek için kullanabilmeleri mümkündür.” ana hipotezi oluşturulmuştur.
Araştırmada kuramsal temelin oluşturulması amacı ile literatür taraması yapılmıştır. Bu aşamada veri madenciliği, veri madenciliği ile ilgili kavramlar, veri madenciliği modelleri, veri madenciliği süreçleri vb. kavramlar yapılan ulusal ve uluslararası çalışmalar doğrultusunda incelenmiş, kütüphanelerde veri madenciliğinin kullanım alanlarına ve uygulamalarına yer verilmiştir.
Araştırma sonucunda veri madenciliği süreciyle elde edilen bulgulara ve değerlendirmelere yer verilmiş, ana hipotez ve alt hipotezler doğrulanmıştır.

Keywords

Veri madenciliği, Büyük veri, Kütüphane hizmetleri, Veri madenciliği modelleri, Veri madenciliği süreçleri

References

• Akçay, A. (2014). Bilgi ve Belge Yönetiminde Veri Madenciliği. Yayımlanmamış yüksek lisans tezi, İstanbul: İstanbul Üniversitesi Sosyal Bilimler Enstitüsü.
• Akdi, Y. (2018). İST308 Zaman Serileri Analizi, 8. Hafta Ders Notları. Ankara Üniversitesi Açık Ders Malzemeleri. Erişim adresi: https://acikders.ankara.edu.tr/mod/resource/view.php?id=47751
• Akpınar, H. (2014). Data Veri Madenciliği Veri Analizi. İstanbul: Papatya Yayıncılık Eğitim.
• Altunkaynak, B. (2022). Veri Madenciliği Yöntemleri ve R Uygulamaları (3. bs.). Ankara: Seçkin Yayıncılık.
• Arslantekin, S. (2003). Veri madenciliği ve bilgi merkezleri. Türk Kütüphaneciliği, 17(4), 369-380. http://www.tk.org.tr/index.php/TK/article/view/303/295
• Brettschneider, P. (2021). Text und data-mining – juristische Fallstricke und bibliothekarische Handlungsfelder. Bibliotheksdienst, 55(2), 104-126.
• Cackett, D. (2013). Information Management and Big Data, A Reference Architecture. Redwood Shores: Oracle Corporation. Erişim adresi: https://citeseerx.ist.psu.edu/pdf/15a6c422db770da6eaecde3fd1630c3eec3880e5
• Chang, C. C. ve Chen, R. S. (2006). Using data mining technology to solve classification problems: A case study of campus digital library. The Electronic Library, 24(3), 307-321.
• Cios, K., Pedrycz, W., Swiniarski, R. ve Kurgan, L. (2007). Data Mining, A Knowledge Discovery Approach. New York: Springer.
• Cox, B. ve Jantti, M. (2012). Discovering the impact of library use and student performance. Educause Review, s. 1-9. Erişim adresi: http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1507&context=asdpapers
• Cullen, K. (2005). Delving into data: Businesses have used data mining for years. Now libraries are getting into the act. Library Journal, 30(13), s. 30-32.
• Data analysis. (t.y.) Cambridge dictionary içinde. Erişim adresi: https://dictionary.cambridge.org/dictionary/english/data-analysis
• Doğan, K. (2022). Bilgi Merkezi ve Hizmetlerinde Veri Madenciliğinin Kullanılabilirliği: Üniversite Kütüphaneleri Örneği. Yayımlanmamış Doktora Tezi, Ankara: Ankara Üniversitesi Sosyal Bilimler Enstitüsü.
• Döring, M. (2018). Prediction vs forecasting. Data Science Blog. Erişim adresi: https://www.datascienceblog.net/post/machine-learning/forecasting_vs_prediction/#:~:text=Prediction%20is%20concerned%20with%20estimating%20the%20outcomes%20for%20unseen%20data.&text=Forecasting%20is%20a%20sub%2Ddiscipline,we%20consider%20the%20temporal%20
• Duan, S. ve Wang, Z. (2021). Research on the service mode of the university library based on data mining. Scientific Programming, 1-9. doi:https://doi.org/10.1155/2021/5564326
• Dunham, M. (2002). Data Mining Introductory and Advanced Topics. New Jersey: Pearson Education Inc.
• Everitt, B. ve Skrondal, A. (2010). The Cambridge Dictionary of Statistics (4. bs.). New York: Cambridge University Press.
• Fayyad, U., Piatetsky-shapiro, G. ve Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 37-54. Erişim adresi: https://www.aaai.org/ojs/index.php/aimagazine/article/viewFile/1230/1131 Gibson, I. E. (2001). Data mining analysis of digital library database usage patterns as a tool facilitating efficient user navigation (Doktora Tezi). Erişim adresi: https://www.proquest.com/dissertations-theses/data-mining-analysis-digital-library-database/docview/251131313/se-2?accountid=8319
• Goodall, D. ve Pattern, D. (2011, 02 22). Academic library non/low use and undergraduate student achievement : A preliminary report of research in progress. Library Management, 32(3), 159-170. Erişim adresi: http://www.emeraldinsight.com/doi/pdfplus/10.1108/01435121111112871
• Girija, N. ve Srivatsa, S. K. (2006). A research study: Using data mining in knowledge base business strategies. Information Technology Journal, 5(3), 590-600.
• Guzman, L. S., Saquicela, V., Ordóñez, E. A., Vandewalle, J. ve Cattrysse, D. (2015). Literature review of data mining applications in academic libraries. The Journal of Academic Librarianship, 41(4), 499-510.
• IBM SPSS Modeler CRISP-DM Guide. (2000). IBM Web Sitesi. Erişim adresi: https://www.ibm.com/docs/en/spss-modeler/18.1.1?topic=spss-modeler-crisp-dm-guide
• Irfiani, E. (2020). Determination of book loan associatıon pattern using apriori algorithm in public libraries. Techno Nusa Mandiri: Journal of Computing and Information Technology, 17(2),137-142.
• Kao, S. C., Chang, H. C. ve Lin, C. H. (2003). Decision support for the academic library acquisition budget allocation via circulation database mining. Information Processing & Management, 39(1), 133-147.
• Katsurai, M. ve Joo, S. (2021). Adoption of data mining methods in the discipline of library and ınformation science. Journal of Library and Information Studies, 19(1), 1-17.
• Khademizadeh, S., Nematollahi, Z. ve Danesh, F. (2022). Analysis of book circulation data and a book recommendation system in academic libraries using data mining techniques. Library and Information Science Research, 44(4), 1-9.
• Kovacevic, A., Devedzic, V. ve Pocajt, V. (2010). using data mining to ımprove digital library services. The Electronic Library, 28(6), 829-843. doi: 10.1108/02640471011093525
• Kurt, L. (2023). Bilgi Yönetiminde Veri ve Metin Madenciliği: Bir Dijital İçerik Analizi Uygulaması. Yayımlanmamış Doktora Tezi, Ankara: Ankara Üniversitesi Sosyal Bilimler Enstitüsü.
• Nicholson, S. (2006). The basis for bibliomining: Frameworks for bringing together usage-based data mining and bibliometrics through data warehousing in digital library services. Information Processing and Management, 42(01), 785-804. doi:10.1016/j.ipm.2005.05.008
• Nisbet, R., Miner, G. ve Yale, K. (2018). Handbook of Statistical Analysis and Data Mining Applications (2. bs.). London: Elsevier.
• Noh, Y. ve Kim, D. (2022). A study on social perceptions of public libraries utilizing the sentiment analysis. International Journal of Knowledge Content Development & Technology, 12(4), 41-65.
• Oded, M. ve Rokach, L. (2010). Data Mining and Knowledge Discovery Handbook (2. bs.). New York: Springer.
• Oğuzlar, A. (2011). Temel Metin Madenciliği. Bursa: Dora.
• Olson, D. (2017). Descriptive Data Mining. Singapore: Springer.
• Olson, D. L. (2018). Data Mining Models, Second Edition. New York: Business Expert Press.
• Olson D. L. ve Araz Ö. M. (2023). Data mining and analytics in healthcare management: Applications and tools. Springer. https://doi.org/10.1007/978-3-031-28113-6
• Papadopoulos, M., Gerolimos, M., Vavousis, K. Xenakis, C. (2020). Text and data mining for the national library of Greece in consideration of ınternet security and GDPR. Qualitative and Quantitative Methods in Libraries (QQML), 9 (3), 441-460.
• Patil, A. ve Gangadhar, N. (2019). Olaas: OLAP as a service. 2016 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), Cloud Computing in Emerging Markets (CCEM) (s. 119-124).
• Bangalore, India: IEEE. Erişim adresi: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7819682
• Pektaş, A. O. (2013). SPSS ile Veri Madenciliği. İstanbul: Dikeyeksen.
• Porritt, G. (2015). Data mining in the humanities and social sciences. Information Today, 18.
• Preza, J. L. (2016). Data Science and Analytics in Libraries. Erişim adresi: https://zenodo.org/record/375809/files/Data%20Science%20and%20Analytics%20in%20Libraries.pdf
• Prytherch, R. (2005). Harrod’s librarians’ glossary and reference book : a dictionary of over 10,200 terms (10 bs.). Hampshire: Ashgate Publishing Limited.
• Quinlan, J. (1986). Induction of Decision Trees. Machine Learning, 81-106. Erişim adresi: https://link.springer.com/content/pdf/10.1007%2FBF00116251.pdf
• Renaud, J., Britton, S., Dingding, W. ve Mitsunori, O. (2015). Mining library and university data to understand library use patterns. The Electronic Library, 33(3), 355-372. https://search.proquest.com/docview/1683340136?accountid=8319
• Roiger, R. J. (2017). Data mining: A tutorial-based primer. Florida: CRC Press, Taylor & Francis Group.
• Sankur, B. (2004). İngilizce - Türkçe Ansiklopedik Bilişim Sözlüğü. İstanbul: Pusula.
• Sathishkumar, S., Devi Priya, R. ve Karthika, K. (2020). Survey on data mining and predictive analytics techniques. G. Ranganathan, J. Chen, ve Á. Rocha (Ed.), Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems (s. 971–981) içinde. Singapore: Springer.
• Shieh, J. C. (2010). The integration system for librarians' bibliomining. The Electronic Library, 28(5), 709-721. Erişim adresi: http://www.emeraldinsight.com/doi/pdfplus/10.1108/02640471011081988
• Soria, K., Fransen, J., ve Nackerud, S. (2013). Library use and undergraduate student outcomes: New evidence for students' retention and academic success. Libraries and the Academy, 13(2), 147-164. Erişim adresi: http://resolver.ebscohost.com/openurl? sid=EBSCO%3aconedsqd7&genre=article&issn=15312542&ISBN=&volume=13&issue=2&date=20130101&spage=147&pages=147-164&title=portal%3a+Libraries+and+the+Academy&atitle=Library+Use+and+Undergraduate+Student+Outcomes%3a+New+Ev
• Stanisz, T., Kwapien, J. ve Drozdz, S. (2019). Linguistic data mining with complex networks: A stylometric-oriented approach. Information Sciences, 482, 301-320. Erişim adresi: https://doi.org/10.1016/j.ins.2019.01.040
• Sun, Y., Sun, J., Du, Q., Zhao, H. ve Liu, J. (2020). Research on personalized service strategy of university library based on big data mining system. 2020 5th International Conference on Smart Grid and Electrical Automation (ICSGEA), (s. 515-518). doi:10.1109/ICSGEA51094.2020.00117
• Şeker, Ş. E. (2013). İş Zekâsı ve Veri Madenciliği. İstanbul: Cinius Yayınları.
• Şeker, Ş. E. (t.y.). MISSözlük. http://mis.sadievrenseker.com/2014/06/metin-madenciligi-text-mining/
• Tan P. N., Steinbach M., Karpatne, A. ve Kumar, V. (2019). Introduction to data mining (2. bs.). Pearson Education Limited.
• T.C. Başbakanlık Devlet İstatistik Enstitüsü Başkanlığı. (t.y.). İstatistik Terimleri Sözlüğü.
• Usman, M., Asghar, S. ve Fong, S. (2010). Data mining and automatic OLAP schema generation. 2010 Fifth International Conference on Digital Information Management (ICDIM) içinde, (s. 35-43). Thunder Bay. doi:10.1109/ICDIM.2010.5664622
• Weng, C.-H. (2016). Knowledge discovery of digital library subscription by RFC itemsets. Electronic Library, 35(5), 772-788. doi:10.1108/EL-06-2015-0086
• Xu, B. (2011). Understanding teacher users of a digital library service: A clustering approach (Doktora tezi). https://www.proquest.com/dissertations-theses/understanding-teacher-users-digital-library/docview/862643655/se-2?accountid=8319
• Yu, C. H. (2022). Data mining and exploration from traditional statistics to modern data science. CRC Press. https://doi.org/10.1201/9781003153658
• Zaki, M. J. ve Meira W. (2020). Data mining and machine learning : fundamental concepts and algorithms (2. bs.). Cambridge University Press.
• Zweig, K. A. (2016). Are Word-Adjacency Networks Networks?. A. Mehler, A. Lücking, S. Banisch, P. Blanchard ve B. Job (Ed.). Towards a Theoretical Framework for Analyzing Complex Linguistic Networks. Understanding Complex Systems içinde (s. 153-163). Berlin: Springer. doi:https://doi.org/10.1007/978-3-662-47238-5_7