Araştırma Makalesi
BibTex RIS Kaynak Göster

Discovering Frequent Keyword Pairs Addressed in Operations Management Related Articles Published Between 2000 and 2016 with Data Mining

Yıl 2017, Cilt: 8 Sayı: 2, 131 - 150, 31.07.2017
https://doi.org/10.18354/esam.314785

Öz



Keywords provide a summarization and
abstraction of document content. Several studies utilized keyword data in
academic publications to find out which concepts had been widely addressed.
Focusing on Operations Management field, this study reveals and introduces the
popular keywords frequently used together in articles published between 2000
and 2016. For this purpose, keyword data of articles published in 39 OM –
related journals were collected into a database. After elimination of redundant
data and standardization of keywords, the most common keywords were identified.
Among unsupervised rule mining techniques, association rule mining was used to
discover frequent keyword pairs in meta-data. The major findings of the study
are important since the keyword pairs correspond to the concepts covered
together in OM-related studies.

Kaynakça

  • Agrawal, R., Imieliński, T. and Swami, A. (1993). Mining Association Rules Between Sets of Items in Large Databases. In ACM SIGMOD Record (Vol. 22, No. 2, pp. 207-216). ACM.
  • Amoako-Gyampah, K. and Meredith, J. R. (1989). The Operations Management Agenda: An Update. Journal of Operations Management, 8 (3), pp. 250-262.
  • Bayardo Jr., R.J. and Agrawal, R. (1999). Mining the Most Interesting Rules. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 145-154). ACM.
  • Filippini, R. (1997). Operations management research: some reflections on evolution, models and empirical studies in OM. International Journal of Operations & Production Management, 17 (7), pp. 655-670.
  • Fry, T.D. and Donohue, J.M. (2013). Outlets for operations management research: a DEA assessment of journal quality and rankings. International Journal of Production Research, 51 (23-24), pp. 7501-7526.
  • HaCohen-Kerner, Y. (2003). Automatic extraction of keywords from abstracts. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, (pp. 843-849). Springer Berlin Heidelberg.
  • Maimon O. and Rokach L. (2005): Data Mining and the Knowledge Discovery Handbook. New York, USA: Springer.
  • Pannirselvam, G. P., Ferguson, L. A., R. Ash, C. and Siferd, S. P. (1999). Operations Management Research: An Update for the 1990s. Journal of Operations Management, 18 (1), pp. 95-112.
  • Pilkington, A. and Fitzgerald, R. (2006). Operations Management Themes, Concepts and Relationships: A Forward Retrospective of IJOPM. International Journal of Operations and Production Management, 26 (11), pp. 1255-1275.
  • Schniederjans, M. J., Schniederjans, A. M. and Schniederjans, D. G. (2009). Operations Research Methodology Life Cycle Trend Phases as Recorded in Journal Articles. Journal of the Operational Research Society, 60 (7), pp. 881-894.
  • Shang, G., Saladin, B., Fry, T. and Donohue, J. (2015). Twenty-six years of operations management research (1985–2010): authorship patterns and research constituents in eleven top rated journals. International Journal of Production Research, 53 (20), pp. 6161-6197.
  • Silverstein, C., Brin, S. and Motwani, R. (1998). Beyond Market Baskets: Generalizing Association Rules to Dependence Rules. Data Mining and Knowledge Discovery, 2, pp. 39-68.
  • Sridhara G., Hill, E., Pollock, L. and Vijay-Shanker, K. (2008). Identifying Word Relations in Software: A Comparative Study of Semantic Similarity Tools. The 16th IEEE International Conference on Program Comprehension. (pp. 123-132). IEEE.
  • Tan P.N., Steinbach M. and Kumar, V. (2005). Introduction to Data Mining. Boston: Addison-Wesley.
  • Taylor A. and Taylor, M. (2009). Operations Management Research: Contemporary Themes, Trends and Potential Future Directions. International Journal of Operations and Production Management, 29 (12), pp. 1316-1340.
  • The United States Census Bureau. (2017). North American Industry Classification System, Retrieved February 03, 2017, from http://www.census.gov/eos/www/naics/.
  • ThoughtWorks. (2017). Selenium – Web Browser Automation, Retrieved February 03, 2017, from http://www.seleniumhq.org/.
  • Tsiptsis, K. and Chorianopoulos, A. (2009). Data Mining Techniques in CRM: Inside Customer Segmentation. Chichester, West Sussex: John Wiley & Sons.

2000-2016 Arası İşlemler Yönetimi Alanıyla İlgili Makalelerde Yaygın Anahtar Kelime Çiftlerinin Veri Madenciliği İle Belirlenmesi

Yıl 2017, Cilt: 8 Sayı: 2, 131 - 150, 31.07.2017
https://doi.org/10.18354/esam.314785

Öz



Anahtar kelimeler, belgelerin
içeriğine dair özetleme ve soyutlama işlevleri görmektedir. Geçmiş çalışmalarda,
İşlemler Yönetimi alanıyla ilgili akademik yayınları konularına ve araştırma yöntemlerine
göre sınıflandıran çalışmalar yer almaktadır. Bununla birlikte, makalelerde anahtar
kelime kullanımının ele alındığı çalışmalara da rastlanmaktadır. Bu çalışmada, İşlemler
Yönetimi (İY) alanına odaklanılarak 2000-2016 arası yayımlanmış makalelerde yaygın
biçimde kullanılmış anahtar kelimelerin ortaya çıkarılması hedeflenmiştir. Bu
amaçla, İY alanıyla ilişkili 39 akademik dergiden makale ve anahtar kelime
verisi toplanarak bir veri tabanı oluşturulmuştur. Gereksiz kayıtların
ayıklanması ve kelimelerin standartlaştırılması sonrasında en sık anahtar
kelimeler bulunmuştur. Sıkça birlikte kullanılan anahtar kelimelerin keşfi
amacıyla, denetimsiz veri madenciliği yöntemleri arasında yer alan birliktelik
kuralları madenciliği kullanılmıştır. Çalışmanın başlıca bulgusu olarak ortaya
çıkarılan anahtar kelime çiftleri, İY alanına ilişkin çalışmalarda sıkça
birlikte değinilen kavramları temsil etmesi yönüyle önem taşımaktadır.




Kaynakça

  • Agrawal, R., Imieliński, T. and Swami, A. (1993). Mining Association Rules Between Sets of Items in Large Databases. In ACM SIGMOD Record (Vol. 22, No. 2, pp. 207-216). ACM.
  • Amoako-Gyampah, K. and Meredith, J. R. (1989). The Operations Management Agenda: An Update. Journal of Operations Management, 8 (3), pp. 250-262.
  • Bayardo Jr., R.J. and Agrawal, R. (1999). Mining the Most Interesting Rules. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 145-154). ACM.
  • Filippini, R. (1997). Operations management research: some reflections on evolution, models and empirical studies in OM. International Journal of Operations & Production Management, 17 (7), pp. 655-670.
  • Fry, T.D. and Donohue, J.M. (2013). Outlets for operations management research: a DEA assessment of journal quality and rankings. International Journal of Production Research, 51 (23-24), pp. 7501-7526.
  • HaCohen-Kerner, Y. (2003). Automatic extraction of keywords from abstracts. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, (pp. 843-849). Springer Berlin Heidelberg.
  • Maimon O. and Rokach L. (2005): Data Mining and the Knowledge Discovery Handbook. New York, USA: Springer.
  • Pannirselvam, G. P., Ferguson, L. A., R. Ash, C. and Siferd, S. P. (1999). Operations Management Research: An Update for the 1990s. Journal of Operations Management, 18 (1), pp. 95-112.
  • Pilkington, A. and Fitzgerald, R. (2006). Operations Management Themes, Concepts and Relationships: A Forward Retrospective of IJOPM. International Journal of Operations and Production Management, 26 (11), pp. 1255-1275.
  • Schniederjans, M. J., Schniederjans, A. M. and Schniederjans, D. G. (2009). Operations Research Methodology Life Cycle Trend Phases as Recorded in Journal Articles. Journal of the Operational Research Society, 60 (7), pp. 881-894.
  • Shang, G., Saladin, B., Fry, T. and Donohue, J. (2015). Twenty-six years of operations management research (1985–2010): authorship patterns and research constituents in eleven top rated journals. International Journal of Production Research, 53 (20), pp. 6161-6197.
  • Silverstein, C., Brin, S. and Motwani, R. (1998). Beyond Market Baskets: Generalizing Association Rules to Dependence Rules. Data Mining and Knowledge Discovery, 2, pp. 39-68.
  • Sridhara G., Hill, E., Pollock, L. and Vijay-Shanker, K. (2008). Identifying Word Relations in Software: A Comparative Study of Semantic Similarity Tools. The 16th IEEE International Conference on Program Comprehension. (pp. 123-132). IEEE.
  • Tan P.N., Steinbach M. and Kumar, V. (2005). Introduction to Data Mining. Boston: Addison-Wesley.
  • Taylor A. and Taylor, M. (2009). Operations Management Research: Contemporary Themes, Trends and Potential Future Directions. International Journal of Operations and Production Management, 29 (12), pp. 1316-1340.
  • The United States Census Bureau. (2017). North American Industry Classification System, Retrieved February 03, 2017, from http://www.census.gov/eos/www/naics/.
  • ThoughtWorks. (2017). Selenium – Web Browser Automation, Retrieved February 03, 2017, from http://www.seleniumhq.org/.
  • Tsiptsis, K. and Chorianopoulos, A. (2009). Data Mining Techniques in CRM: Inside Customer Segmentation. Chichester, West Sussex: John Wiley & Sons.
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

İnanç Kabasakal

Aydın Koçak

Yayımlanma Tarihi 31 Temmuz 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 8 Sayı: 2

Kaynak Göster

APA Kabasakal, İ., & Koçak, A. (2017). 2000-2016 Arası İşlemler Yönetimi Alanıyla İlgili Makalelerde Yaygın Anahtar Kelime Çiftlerinin Veri Madenciliği İle Belirlenmesi. Ege Stratejik Araştırmalar Dergisi, 8(2), 131-150. https://doi.org/10.18354/esam.314785
AMA Kabasakal İ, Koçak A. 2000-2016 Arası İşlemler Yönetimi Alanıyla İlgili Makalelerde Yaygın Anahtar Kelime Çiftlerinin Veri Madenciliği İle Belirlenmesi. ESAM. Temmuz 2017;8(2):131-150. doi:10.18354/esam.314785
Chicago Kabasakal, İnanç, ve Aydın Koçak. “2000-2016 Arası İşlemler Yönetimi Alanıyla İlgili Makalelerde Yaygın Anahtar Kelime Çiftlerinin Veri Madenciliği İle Belirlenmesi”. Ege Stratejik Araştırmalar Dergisi 8, sy. 2 (Temmuz 2017): 131-50. https://doi.org/10.18354/esam.314785.
EndNote Kabasakal İ, Koçak A (01 Temmuz 2017) 2000-2016 Arası İşlemler Yönetimi Alanıyla İlgili Makalelerde Yaygın Anahtar Kelime Çiftlerinin Veri Madenciliği İle Belirlenmesi. Ege Stratejik Araştırmalar Dergisi 8 2 131–150.
IEEE İ. Kabasakal ve A. Koçak, “2000-2016 Arası İşlemler Yönetimi Alanıyla İlgili Makalelerde Yaygın Anahtar Kelime Çiftlerinin Veri Madenciliği İle Belirlenmesi”, ESAM, c. 8, sy. 2, ss. 131–150, 2017, doi: 10.18354/esam.314785.
ISNAD Kabasakal, İnanç - Koçak, Aydın. “2000-2016 Arası İşlemler Yönetimi Alanıyla İlgili Makalelerde Yaygın Anahtar Kelime Çiftlerinin Veri Madenciliği İle Belirlenmesi”. Ege Stratejik Araştırmalar Dergisi 8/2 (Temmuz 2017), 131-150. https://doi.org/10.18354/esam.314785.
JAMA Kabasakal İ, Koçak A. 2000-2016 Arası İşlemler Yönetimi Alanıyla İlgili Makalelerde Yaygın Anahtar Kelime Çiftlerinin Veri Madenciliği İle Belirlenmesi. ESAM. 2017;8:131–150.
MLA Kabasakal, İnanç ve Aydın Koçak. “2000-2016 Arası İşlemler Yönetimi Alanıyla İlgili Makalelerde Yaygın Anahtar Kelime Çiftlerinin Veri Madenciliği İle Belirlenmesi”. Ege Stratejik Araştırmalar Dergisi, c. 8, sy. 2, 2017, ss. 131-50, doi:10.18354/esam.314785.
Vancouver Kabasakal İ, Koçak A. 2000-2016 Arası İşlemler Yönetimi Alanıyla İlgili Makalelerde Yaygın Anahtar Kelime Çiftlerinin Veri Madenciliği İle Belirlenmesi. ESAM. 2017;8(2):131-50.