Skip to main content
SpringerLink
Log in

A comprehensive survey of data mining

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Data mining plays an important role in various human activities because it extracts the unknown useful patterns (or knowledge). Due to its capabilities, data mining become an essential task in large number of application domains such as banking, retail, medical, insurance, bioinformatics, etc. To take a holistic view of the research trends in the area of data mining, a comprehensive survey is presented in this paper. This paper presents a systematic and comprehensive survey of various data mining tasks and techniques. Further, various real-life applications of data mining are presented in this paper. The challenges and issues in area of data mining research are also presented in this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Fayadd U, Piatesky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AAAI Press/The MIT Press, Massachusetts Institute of Technology. ISBN 0–262 56097–6 Fayap

  2. Fayadd U, Piatesky-Shapiro G, Smyth P (1996) Knowledge discovery and data mining: towards a unifying framework. In: Proceedings of the 2nd ACM international conference on knowledge discovery and data mining (KDD), Portland, pp 82–88

  3. Heikki M (1996) Data mining: machine learning, statistics, and databases. In: SSDBM ’96: proceedings of the eighth international conference on scientific and statistical database management, June 1996, pp 2–9

  4. Arora RK, Gupta MK (2017) e-Governance using data warehousing and data mining. Int J Comput Appl 169(8):28–31

    Google Scholar 

  5. Morik K, Bhaduri K, Kargupta H (2011) Introduction to data mining for sustainability. Data Min Knowl Discov 24(2):311–324

    Google Scholar 

  6. Han J, Kamber M, Pei J (2012) Data mining concepts and techniques, 3rd edn. Elsevier, Netherlands

    MATH  Google Scholar 

  7. Friedman JH (1997) Data mining and statistics: What is the connection? in: Keynote Speech of the 29th Symposium on the Interface: Computing Science and Statistics, Houston, TX, 1997

  8. Turban E, Aronson JE, Liang TP, Sharda R (2007) Decision support and business intelligence systems. 8th edn, Pearson Education, UK

  9. Gheware SD, Kejkar AS, Tondare SM (2014) Data mining: tasks, tools, techniques and applications. Int J Adv Res Comput Commun Eng 3(10):8095–8098

    Google Scholar 

  10. Kiranmai B, Damodaram A (2014) A review on evaluation measures for data mining tasks. Int J Eng Comput Sci 3(7):7217–7220

    Google Scholar 

  11. Sharma M (2014) Data mining: a literature survey. Int J Emerg Res Manag Technol 3(2):1–4

    Google Scholar 

  12. Venkatadri M, Reddy LC (2011) A review on data mining from past to the future. Int J Comput Appl 15(7):19–22

    Google Scholar 

  13. Chen M, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866–883

    Google Scholar 

  14. Gupta MK, Chandra P (2019) A comparative study of clustering algorithms. In: Proceedings of the 13th INDIACom-2019; IEEE Conference ID: 461816; 6th International Conference on “Computing for Sustainable Global Development”

  15. Ponniah P (2001) Data warehousing fundamentals. Wiley, USA

    Google Scholar 

  16. Chandra P, Gupta MK (2018) Comprehensive survey on data warehousing research. Int J Inform Technol 10(2):217–224

    Google Scholar 

  17. Weiss SH, Indurkhya N (1998) Predictive data mining: a practical guide. Morgan Kaufmann Publishers, San Francisco

    MATH  Google Scholar 

  18. Fu Y (1997) Data mining: tasks, techniques, and applications. IEEE Potentials 16(4):18–20

    Google Scholar 

  19. Abuaiadah D (2015) Using bisect k-means clustering technique in the analysis of arabic documents. ACM Trans Asian Low-Resour Lang Inf Process 15(3):1–17

    Google Scholar 

  20. Algergawy A, Mesiti M, Nayak R, Saake G (2011) XML data clustering: an overview. ACM Comput Surv 43(4):1–25

    MATH  Google Scholar 

  21. Angiulli F, Fassetti F (2013) Exploiting domain knowledge to detect outliers. Data Min Knowl Discov 28(2):519–568

    MathSciNet  MATH  Google Scholar 

  22. Angiulli F, Fassetti F (2016) Toward generalizing the unification with statistical outliers: the gradient outlier factor measure. ACM Trans Knowl Discov Data 10(3):1–26

    Google Scholar 

  23. Bhatnagar V, Ahuja S, Kaur S (2015) Discriminant analysis-based cluster ensemble. Int J Data Min Modell Manag 7(2):83–107

    Google Scholar 

  24. Bouguessa M (2013) Clustering categorical data in projected spaces. Data Min Knowl Discov 29(1):3–38

    MathSciNet  Google Scholar 

  25. Campello RJGB, Moulavi D, Zimek A, Sander J (2015) Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Trans Knowl Discov Data 10(1):1–51

    Google Scholar 

  26. Carpineto C, Osinski S, Romano G, Weiss D (2009) A survey of web clustering engines. ACM Comput. Surv. 41(3):1–38

    Google Scholar 

  27. Ceglar A, Roddick JF (2006) Association mining. ACM Comput Surv 38(2):1–42

    Google Scholar 

  28. Chen YL, Weng CH (2009) Mining fuzzy association rules from questionnaire data. Knowl Based Syst 22(1):46–56

    Google Scholar 

  29. Fan Chin-Yuan, Fan Pei-Shu, Chan Te-Yi, Chang Shu-Hao (2012) Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals. Expert Syst Appl 39:8844–8851

    Google Scholar 

  30. Das R, Kalita J, Bhattacharya (2011) A pattern matching approach for clustering gene expression data. Int J Data Min Model Manag 3(2):130–149

    Google Scholar 

  31. Dincer E (2006) The k-means algorithm in data mining and an application in medicine. Kocaeli Univesity, Kocaeli

    Google Scholar 

  32. Geng L, Hamilton HJ (2006) Interestingness measures for data mining: a survey. ACM Comput Surv 38(3):1–32

    Google Scholar 

  33. Gupta MK, Chandra P (2019) P-k-means: k-means using partition based cluster initialization method. In: Proceedings of the international conference on advancements in computing and management (ICACM 2019), Elsevier SSRN, pp 567–573

  34. Gupta MK, Chandra P (2019) An empirical evaluation of k-means clustering algorithm using different distance/similarity metrics. In: Proceedings of the international conference on emerging trends in information technology (ICETIT-2019), emerging trends in information technology, LNEE 605 pp 884–892 DOI: https://doi.org/10.1007/978-3-030-30577-2_79

  35. Hea Z, Xua X, Huangb JZ, Denga S (2004) Mining class outliers: concepts, algorithms and applications in CRM. Expert Syst Appl 27(4):681e97

    Google Scholar 

  36. Hung LN, Thu TNT, Nguyen GC (2015) An efficient algorithm in mining frequent itemsets with weights over data stream using tree data structure. IJ Intell Syst Appl 12:23–31

    Google Scholar 

  37. Hung LN, Thu TNT (2016) Mining frequent itemsets with weights over data stream using inverted matrix. IJ Inf Technol Comput Sci 10:63–71

    Google Scholar 

  38. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput. Surv 31(3):1–60

    Google Scholar 

  39. Jin H, Wang S, Zhou Q, Li Y (2014) An improved method for density-based clustering. Int J Data Min Model Manag 6(4):347–368

    Google Scholar 

  40. Khandare A, Alvi AS (2017) Performance analysis of improved clustering algorithm on real and synthetic data. IJ Comput Netw Inf Secur 10:57–65

    Google Scholar 

  41. Koh YS, Ravana SD (2016) Unsupervised rare pattern mining: a survey. ACM Trans Knowl Discov Data 10(4):1–29

    Google Scholar 

  42. Kosina P, Gama J (2015) Very fast decision rules for classification in data streams. Data Min Knowl Discov 29(1):168–202

    MathSciNet  Google Scholar 

  43. Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268

    MathSciNet  MATH  Google Scholar 

  44. Kumar D, Bezdek JC, Rajasegarar S, Palaniswami M, Leckie C, Chan J, Gubbi J (2016) Adaptive cluster tendency visualization and anomaly detection for streaming data. ACM Trans Knowl Discov Data 11(2):1–24

    Google Scholar 

  45. Lee G, Yun U (2017) A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives. Future Gener Comput Syst 68:89–110

    Google Scholar 

  46. Li G, Zaki MJ (2015) Sampling frequent and minimal boolean patterns: theory and application in classification. Data Min Knowl Discov 30(1):181–225. https://doi.org/10.1007/s10618-015-0409-y

    Article  MathSciNet  MATH  Google Scholar 

  47. Liao TW, Triantaphyllou E (2007) Recent advances in data mining of enterprise data: algorithms and applications. World Scientific Publishing, Singapore, pp 111–145

    Google Scholar 

  48. Mabroukeh NR, Ezeife CI (2010) A taxonomy of sequential pattern mining algorithms. ACM Comput Surv 43:1

    Google Scholar 

  49. Mampaey M, Vreeken J (2011) Summarizing categorical data by clustering attributes. Data Min Knowl Discov 26(1):130–173

    MathSciNet  MATH  Google Scholar 

  50. Menardi G, Torelli N (2012) Training and assessing classification rules with imbalanced data. Data Min Knowl Discov 28(1):4–28. https://doi.org/10.1007/s10618-012-0295-5

    Article  MathSciNet  MATH  Google Scholar 

  51. Mukhopadhyay A, Maulik U, Bandyopadhyay S (2015) A survey of multiobjective evolutionary clustering. ACM Comput Surv 47(4):1–46

    Google Scholar 

  52. Pei Y, Fern XZ, Tjahja TV, Rosales R (2016) ‘Comparing clustering with pairwise and relative constraints: a unified framework. ACM Trans Knowl Discov Data 11:2

    Google Scholar 

  53. Rafalak M, Deja M, Wierzbicki A, Nielek R, Kakol M (2016) Web content classification using distributions of subjective quality evaluations. ACM Trans Web 10:4

    Google Scholar 

  54. Reddy D, Jana PK (2014) A new clustering algorithm based on Voronoi diagram. Int J Data Min Model Manag 6(1):49–64

    Google Scholar 

  55. Rustogi S, Sharma M, Morwal S (2017) Improved Parallel Apriori Algorithm for Multi-cores. IJ Inf Technol Comput Sci 4:18–23

    Google Scholar 

  56. Shah-Hosseini H (2013) Improving K-means clustering algorithm with the intelligent water drops (IWD) algorithm. Int J Data Min Model Manag 5(4):301–317

    Google Scholar 

  57. Silva JA, Faria ER, Barros RC, Hruschka ER, de Carvalho ACPLF, Gama J (2013) Data stream clustering: a survey. ACM Comput Surv 46(1):1–31

    MATH  Google Scholar 

  58. Silva A, Antunes C (2014) Multi-relational pattern mining over data streams. Data Min Knowl Discov 29(6):1783–1814. https://doi.org/10.1007/s10618-014-0394-6

    Article  MathSciNet  MATH  Google Scholar 

  59. Sim K, Gopalkrishnan V, Zimek A, Cong G (2012) A survey on enhanced subspace clustering. Data Min Knowl Discov 26(2):332–397

    MathSciNet  MATH  Google Scholar 

  60. Sohrabi MK, Roshani R (2017) Frequent itemset mining using cellular learning automata. Comput Hum Behav 68:244–253

    Google Scholar 

  61. Craw Susan, Wiratunga Nirmalie, Rowe Ray C (2006) Learning adaptation knowledge to improve case-based reasoning. Artif Intell 170:1175–1192

    MathSciNet  MATH  Google Scholar 

  62. Tan KC, Teoh EJ, Yua Q, Goh KC (2009) A hybrid evolutionary algorithm for attribute selection in data mining. Expert Syst Appl 36(4):8616–8630

    Google Scholar 

  63. Tew C, Giraud-Carrier C, Tanner K, Burton S (2013) Behavior-based clustering and analysis of interestingness measures for association rule mining. Data Min Knowl Discov 28(4):1004–1045

    MathSciNet  MATH  Google Scholar 

  64. Wang L, Dong M (2015) Exemplar-based low-rank matrix decomposition for data clustering. Data Min Knowl Discov 29:324–357

    MathSciNet  MATH  Google Scholar 

  65. Wang F, Sun J (2014) Survey on distance metric learning and dimensionality reduction in data mining. Data Min Knowl Discov 29:534–564

    MathSciNet  MATH  Google Scholar 

  66. Wang B, Rahal I, Dong A (2011) Parallel hierarchical clustering using weighted confidence affinity. Int J Data Min Model Manag 3(2):110–129

    Google Scholar 

  67. Zacharis NZ (2018) Classification and regression trees (CART) for predictive modeling in blended learning. IJ Intell Syst Appl 3:1–9

    Google Scholar 

  68. Zhang W, Li R, Feng D, Chernikov A, Chrisochoides N, Osgood C, Ji S (2015) Evolutionary soft co-clustering: formulations, algorithms, and applications. Data Min Knowl Discov 29:765–791

    MathSciNet  MATH  Google Scholar 

  69. Han J, Fu Y (1996) Exploration of the power of attribute-oriented induction in data mining. Adv Knowl Discov Data Min. AAAI/MIT Press, pp 399-421

  70. Gupta A, Mumick IS (1995) Maintenance of materialized views: problems, techniques, and applications. IEEE Data Eng Bull 18(2):3

    Google Scholar 

  71. Sawant V, Shah K (2013) A review of distributed data mining using agents. Int J Adv Technol Eng Res 3(5):27–33

    Google Scholar 

  72. Gupta MK, Chandra P (2019) An efficient approach for selection of initial cluster centroids for k-means clustering algorithm. In: Proceedings international conference on recent developments in science engineering and technology (REDSET-2019), November 15–16 2019

  73. Gupta MK, Chandra P (2019) MP-K-means: modified partition based cluster initialization method for k-means algorithm. Int J Recent Technol Eng 8(4):1140–1148

    Google Scholar 

  74. Gupta MK, Chandra P (2019) HYBCIM: hypercube based cluster initialization method for k-means. IJ Innov Technol Explor Eng 8(10):3584–3587. https://doi.org/10.35940/ijitee.j9774.0881019

    Article  Google Scholar 

  75. Enke David, Thawornwong Suraphan (2005) The use of data mining and neural networks for forecasting stock market returns. Expert Syst Appl 29:927–940

    Google Scholar 

  76. Mezyk Edward, Unold Olgierd (2011) Machine learning approach to model sport training. Comput Hum Behav 27:1499–1506

    Google Scholar 

  77. Esling P, Agon C (2012) Time-series data mining. ACM Comput Surv 45(1):1–34

    MATH  Google Scholar 

  78. Hüllermeier Eyke (2005) Fuzzy methods in machine learning and data mining: status and prospects. Fuzzy Sets Syst 156:387–406

    MathSciNet  Google Scholar 

  79. Hullermeier Eyke (2011) Fuzzy sets in machine learning and data mining. Appl Soft Comput 11:1493–1505

    Google Scholar 

  80. Gengshen Du, Ruhe Guenther (2014) Two machine-learning techniques for mining solutions of the ReleasePlanner™ decision support system. Inf Sci 259:474–489

    Google Scholar 

  81. Smith Kate A, Gupta Jatinder ND (2000) Neural networks in business: techniques and applications for the operations researcher. Comput Oper Res 27:1023–1044

    MATH  Google Scholar 

  82. Huang Mu-Jung, Tsou Yee-Lin, Lee Show-Chin (2006) Integrating fuzzy data mining and fuzzy artificial neural networks for discovering implicit knowledge. Knowl Based Syst 19:396–403

    Google Scholar 

  83. Padhraic S (2000) Data mining: analysis on grand scale. Stat Method Med Res 9(4):309–327. https://doi.org/10.1191/096228000701555181

    Article  MATH  Google Scholar 

  84. Saeed S, Ali M (2012) Privacy-preserving back-propagation and extreme learning machine algorithms. Data Knowl Eng 79–80:40–61

    Google Scholar 

  85. Singh Y, Bhatia PK, Sangwan OP (2007) A review of studies on machine learning techniques. Int J Comput Sci Secur 1(1):70–84

    Google Scholar 

  86. Yahia ME, El-taher ME (2010) A new approach for evaluation of data mining techniques. Int J Comput Sci Issues 7(5):181–186

    Google Scholar 

  87. Jackson J (2002) Data mining: a conceptual overview. Commun Assoc Inf Syst 8:267–296

    Google Scholar 

  88. Heckerman D (1998) A tutorial on learning with Bayesian networks. Learning in graphical models. Springer, Netherlands, pp 301–354

    MATH  Google Scholar 

  89. Politano PM, Walton RO (2017) Statistics & research methodol. Lulu. com

  90. Wetherill GB (1987) Regression analysis with application. Chapman & Hall Ltd, UK

    Google Scholar 

  91. Anderberg MR (2014) Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks, vol 19. Academic Press, USA

    Google Scholar 

  92. Mihoci A (2017) Modelling limit order book volume covariance structures. In: Hokimoto T (ed) Advances in statistical methodologies and their application to real problems. IntechOpen, Croatia. https://doi.org/10.5772/66152

    Chapter  Google Scholar 

  93. Thompson B (2004) Exploratory and confirmatory factor analysis: understanding concepts and applications. American Psychological Association, Washington, DC (ISBN:1-59147-093-5)

    Google Scholar 

  94. Kuzey C, Uyar A, Delen (2014) The impact of multinationality on firm value: a comparative analysis of machine learning techniques. Decis Support Syst 59:127–142

    Google Scholar 

  95. Chan Philip K, Salvatore JS (1997) On the accuracy of meta-learning for scalable data mining. J Intell Inf Syst 8:5–28

    Google Scholar 

  96. Tsai Chih-Fong, Hsu Yu-Feng, Lin Chia-Ying, Lin Wei-Yang (2009) Intrusion detection by machine learning: a review. Expert Syst Appl 36:11994–12000

    Google Scholar 

  97. Liao SH, Chu PH, Hsiao PY (2012) Data mining techniques and applications—a decade review from 2000 to 2011. Expert Syst Appl 39:11303–11311

    Google Scholar 

  98. Kanevski M, Parkin R, Pozdnukhov A, Timonin V, Maignan M, Demyanov V, Canu S (2004) Environmental data mining and modelling based on machine learning algorithms and geostatistics. Environ Model Softw 19:845–855

    Google Scholar 

  99. Jain N, Srivastava V (2013) Data mining techniques: a survey paper. Int J Res Eng Technol 2(11):116–119

    Google Scholar 

  100. Baker RSJ (2010) Data mining for education. In: McGaw B, Peterson P, Baker E (eds) International encyclopedia of education, 3rd edn. Elsevier, Oxford, UK

    Google Scholar 

  101. Lew A, Mauch H (2006) Introduction to data mining and its applications. Springer, Berlin

    Google Scholar 

  102. Mukherjee S, Shaw R, Haldar N, Changdar S (2015) A survey of data mining applications and techniques. Int J Comput Sci Inf Technol 6(5):4663–4666

    Google Scholar 

  103. Data mining examples: most common applications of data mining (2019). https://www.softwaretestinghelp.com/data-mining-examples/. Accessed 27 Dec 2019

  104. Devi SVSG (2013) Applications and trends in data mining. Orient J Comput Sci Technol 6(4):413–419

    Google Scholar 

  105. Data mining—applications & trends. https://www.tutorialspoint.com/data_mining/dm_applications_trends.htm

  106. Keleş MK (2017) An overview: the impact of data mining applications on various sectors. Tech J 11(3):128–132

    Google Scholar 

  107. Top 14 useful applications for data mining. https://bigdata-madesimple.com/14-useful-applications-of-data-mining/. Accessed 20 Aug 2014

  108. Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inf Technol Decis Making 5(4):597–604

    Google Scholar 

  109. Padhy N, Mishra P, Panigrahi R (2012) A survey of data mining applications and future scope. Int J Comput Sci Eng Inf Technol 2(3):43–58

    Google Scholar 

  110. Gibert K, Sanchez-Marre M, Codina V (2010) Choosing the right data mining technique: classification of methods and intelligent recommendation. In: International Congress on Environment Modelling and Software Modelling for Environment’s Sake, Fifth Biennial Meeting, Ottawa, Canada

Download references

Author information

Authors and Affiliations

  1. University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Sector-16C, Dwarka, Delhi, 110078, India

    Manoj Kumar Gupta & Pravin Chandra

Authors
  1. Manoj Kumar Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pravin Chandra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manoj Kumar Gupta.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, M.K., Chandra, P. A comprehensive survey of data mining. Int. j. inf. tecnol. 12, 1243–1257 (2020). https://doi.org/10.1007/s41870-020-00427-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-020-00427-7

Keywords

Search

Navigation