Using Data Mining Strategy in Qualitative Research

  • Nadhirah Rasid
  • Puteri N. E. Nohuddin
  • Hamidah Alias
  • Irna Hamzah
  • A. Imran Nordin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10645)


Analyzing qualitative data can be tedious if it is done manually. There are several techniques available to conduct qualitative research such as thematic analysis, grounded theory and content analysis amongst other techniques. The data collected from these techniques are usually huge in amount. Little has been done to apply data mining strategy to analyzes data gathered using qualitative methodology. In this paper, we present a work done to apply text mining technique to analyzes data gathered from interviews – unstructured data. The aim of this study is to develop patterns of pediatric cancer patient’s activities in the ward. The result shows a pattern that suggests patients are mostly playing video games while receiving treatment and when they feel bored in the ward. This proposes that data mining techniques can be used to provide an initial insight of the information gathered qualitatively.


Experience mining Text mining Pediatric Cancer Interview 


  1. 1.
    Woodgate, R.: Part I: an introduction to conducting qualitative research in children with cancer. J. Pediatr. Oncol. Nurs. 17, 192–206 (2000)CrossRefGoogle Scholar
  2. 2.
    Rama Prasath, A., Ramya, M.M.: Automated drusen grading system in fundus image using fuzzy c-means clustering. Int. J. Eng. Technol. 6, 833–841 (2014)Google Scholar
  3. 3.
    Zhu, F., Patumcharoenpol, P., Zhang, C., Yang, Y., Chan, J., Meechai, A., Vongsangnak, W., Shen, B.: Biomedical text mining and its applications in cancer research. J. Biomed. Inform. 46, 200–211 (2013)CrossRefGoogle Scholar
  4. 4.
    Katal, A., Wazid, M., Goudar, R.H.: Big data: issues, challenges, tools and good practices. In: 2013 6th International Conference Contemporary Computing, IC3 2013, pp. 404–409 (2013)Google Scholar
  5. 5.
    Malterud, K.: Qualitative research: standards, challenges, and guidelines. Lancet 358, 483–488 (2001)CrossRefGoogle Scholar
  6. 6.
    Zainal, A.O., Nor Saleha, I.T.: National Cancer Registry Report (2011)Google Scholar
  7. 7.
    Lee, D.W., Kochenderfer, J.N., Stetler-Stevenson, M., Cui, Y.K., Delbrook, C., Feldman, S.A., Fry, T.J., Orentas, R., Sabatino, M., Shah, N.N., Steinberg, S.M., Stroncek, D., Tschernia, N., Yuan, C., Zhang, H., Zhang, L., Rosenberg, S.A., Wayne, A.S., Mackall, C.L.: T cells expressing CD19 chimeric antigen receptors for acute lymphoblastic leukaemia in children and young adults: a phase 1 dose-escalation trial. Lancet 385, 517–528 (2015)CrossRefGoogle Scholar
  8. 8.
    Taylor, R.M., Pearce, S., Gibson, F., Fern, L., Whelan, J.: Developing a conceptual model of teenage and young adult experiences of cancer through meta-synthesis. Int. J. Nurs. Stud. 50, 832–846 (2013)CrossRefGoogle Scholar
  9. 9.
    Tong, A., Palmer, S., Manns, B., Craig, J.C., Ruospo, M., Gargano, L., Johnson, D.W., Hegbrant, J., Olsson, M., Fishbane, S., Strippoli, G.F.M.: The beliefs and expectations of patients and caregivers about home haemodialysis: an interview study. BMJ Open 3, e002148 (2013)Google Scholar
  10. 10.
    Wade, D.M., Brewin, C.R., Howell, D.C.J., White, E., Mythen, M.G., Weinman, J.A.: Intrusive memories of hallucinations and delusions in traumatized intensive care patients: an interview study. Br. J. Health. Psychol. 20, 613–631 (2015)CrossRefGoogle Scholar
  11. 11.
    Hrastinski, S., Aghaee, N.M.: How are campus students using social media to support their studies? An explorative interview study. Educ. Inf. Technol. 17, 451–464 (2012)CrossRefGoogle Scholar
  12. 12.
    Iosup, A., Epema, D.: An experience report on using gamification in technical higher education. In: Proceedings of 45th ACM Technical Symposium on Computer Science Education, SIGCSE 2014, pp. 27–32 (2014)Google Scholar
  13. 13.
    Ching, L.: A quantitative investigation of narratives: recycled drinking water. Water Policy 17, 831–847 (2015)CrossRefGoogle Scholar
  14. 14.
    Castellani, B., Castellani, J.: Data mining: qualitative analysis with health informatics data. Qual. Health Res. 13, 1005–1018 (2003)CrossRefGoogle Scholar
  15. 15.
    Alam, F., Pachauri, S.: Comparative study of J48, Naive Bayes and One-R classification technique for credit card fraud detection using WEKA. Adv. Comput. Sci. Technol. 10, 1731–1743 (2017)Google Scholar
  16. 16.
    Fan, C., Xiao, F., Wang, S.: Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques. Appl. Energy 127, 1–10 (2014)CrossRefGoogle Scholar
  17. 17.
    Slimani, T., Lazzez, A.: Efficient Analysis of Pattern and Association Rule Mining Approaches. arXiv preprint arXiv:1402.2892 (2014)
  18. 18.
    He, W.: Examining students’ online interaction in a live video streaming environment using data mining and text mining. Comput. Hum. Behav. 29, 90–102 (2013)CrossRefGoogle Scholar
  19. 19.
    Nohuddin, P.N.E., Coenen, F., Christley, R., Setzkorn, C.: Detecting temporal pattern and cluster changes in social networks: a study focusing UK cattle movement database. In: Shi, Z., Vadera, S., Aamodt, A., Leake, D. (eds.) Intelligent Information Processing V, IIP 2010. IFIP Advances in Information and Communication Technology, vol. 340, pp. 163–172. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-16327-2_22
  20. 20.
    Vijiyarani, S., Sudha, S.: Disease prediction in data mining technique – a survey. Int. J. Comput. Appl. Inf. Technol. 2, 17–21 (2013)Google Scholar
  21. 21.
    Ahmed, K., Emran, A.A., Jesmin, T., Mukti, R.F., Rahman, M.Z., Ahmed, F.: Early detection of lung cancer risk using data mining. Asian Pac. J. Cancer Prev. 14, 595–598 (2013)Google Scholar
  22. 22.
    Eynon, R., Hjorth, I., Yasseri, T., Gillani, N.: Understanding Communication Patterns in MOOCs: Combining Data Mining and qualitative methods. arXiv preprint arXiv:1607.07495 (2016)
  23. 23.
    Dangare, C.S., Apte, S.S.: Improved study of heart disease prediction system using data mining classification techniques. Int. J. Comput. Appl. 0975–888 47, 44–48 (2012)Google Scholar
  24. 24.
    Yukselturk, E., Ozekes, S., Türel, Y.K.: Predicting dropout student : an application of data mining methods in an online education program. Eur. J. Open Distance e‐Learning 17, 118–133 (2014)Google Scholar
  25. 25.
    Khadjeh Nassirtoussi, A., Aghabozorgi, S., Ying Wah, T., Ngo, D.C.L.: Text mining of news-headlines for FOREX market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment. Expert Syst. Appl. 42, 306–324 (2015)CrossRefGoogle Scholar
  26. 26.
    He, W., Zha, S., Li, L.: Social media competitive analysis and text mining: a case study in the pizza industry. Int. J. Inf. Manag. 33, 464–472 (2013)CrossRefGoogle Scholar
  27. 27.
    Bajaj, K., Pattabiraman, K., Mesbah, A.: Mining questions asked by web developers. In: Proceedings 11th Working Conference Mining Software Repositories - MSR 2014, pp. 112–121 (2014)Google Scholar
  28. 28.
    Berezina, K., Bilgihan, A., Cobanoglu, C., Okumus, F.: Understanding satisfied and dissatisfied hotel customers: text mining of online hotel reviews. J. Hosp. Mark. Manag. 25, 1–24 (2015)Google Scholar
  29. 29.
    Khadjeh Nassirtoussi, A., Aghabozorgi, S., Ying Wah, T., Ngo, D.C.L.: Text mining for market prediction: a systematic review. Expert Syst. Appl. 41, 7653–7670 (2014)Google Scholar
  30. 30.
    Rose, J., Lennerholt, C.: Low cost text mining as a strategy for qualitative researchers. Electron. J. Bus. Res. Method 15, 2–16 (2017)Google Scholar
  31. 31.
    Yu, C.H., Jannasch-Pennell, A., Digangi, S., Yu, C.H.: Compatibility between text mining and qualitative research in the perspectives of grounded theory, content analysis, and reliability. Qual. Rep. 16, 730–744 (2011)Google Scholar
  32. 32.
    Berglund, M., Westin, L., Svanstrom, R., Sundler, A.J.: Suffering caused by care patients’ experiences from hospital settings. Int. J. Qual. Stud. Health Well-Being 7, 1–9 (2012)CrossRefGoogle Scholar
  33. 33.
    Turnbull, M.: A qualitative investigation into the experiences, perceptions, beliefs and self-care management of people with type 2 diabetes (2015)Google Scholar
  34. 34.
    Morton, K., Balazinska, M., Grossman, D., Mackinlay, J.: Support the data enthusiast. Proc. VLDB Endow. 7, 453–456 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nadhirah Rasid
    • 1
  • Puteri N. E. Nohuddin
    • 1
  • Hamidah Alias
    • 2
  • Irna Hamzah
    • 1
  • A. Imran Nordin
    • 1
  1. 1.Institute of Visual InformaticsUniversiti Kebangsaan MalaysiaBangiMalaysia
  2. 2.Department of Pediatrics, Faculty of MedicineUniversiti Kebangsaan MalaysiaKuala LumpurMalaysia

Personalised recommendations