Skip to main content
SpringerLink
Log in

Top 10 algorithms in data mining

  • Survey Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

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. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB conference, pp 487–499

  2. Ahmed S, Coenen F and Leng PH (2006). Tree-based partitioning of date for association rule mining. Knowl Inf Syst 10(3): 315–331

    Article  Google Scholar 

  3. Banerjee A, Merugu S, Dhillon I and Ghosh J (2005). Clustering with Bregman divergences. J Mach Learn Res 6: 1705–1749

    MathSciNet  Google Scholar 

  4. Bezdek JC, Chuah SK, Leep D (1986) Generalized k-nearest neighbor rules. Fuzzy Sets Syst 18(3):237–256. http://dx.doi.org/10.1016/0165-0114(86)90004-7

  5. Bloch DA, Olshen RA and Walker MG (2002). Risk estimation for classification trees. J Comput Graph Stat 11: 263–288

    Article  MathSciNet  Google Scholar 

  6. Bonchi F and Lucchese C (2006). On condensed representations of constrained frequent patterns. Knowl Inf Syst 9(2): 180–201

    Article  Google Scholar 

  7. Breiman L (1968) Probability theory. Addison-Wesley, Reading. Republished (1991) in Classics of mathematics. SIAM, Philadelphia

  8. Breiman L (1999). Prediction games and arcing classifiers. Neural Comput 11(7): 1493–1517

    Article  Google Scholar 

  9. Breiman L, Friedman JH, Olshen RA and Stone CJ (1984). Classification and regression trees. Wadsworth, Belmont

    MATH  Google Scholar 

  10. Brin S and Page L (1998). The anatomy of a large-scale hypertextual Web Search Sngine. Comput Networks 30(1–7): 107–117

    Google Scholar 

  11. Chen JR (2007). Making clustering in delay-vector space meaningful. Knowl Inf Syst 11(3): 369–385

    Article  Google Scholar 

  12. Cheung DW, Han J, Ng V, Wong CY (1996) Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings of the ACM SIGMOD international conference on management of data, pp. 13–23

  13. Chi Y, Wang H, Yu PS and Muntz RR (2006). Catch the moment: maintaining closed frequent itemsets over a data stream sliding window. Knowl Inf Syst 10(3): 265–294

    Article  Google Scholar 

  14. Cost S, Salzberg S (1993) A weighted nearest neighbor algorithm for learning with symbolic features. Mach Learn 10:57.78 (PEBLS: Parallel Examplar-Based Learning System)

    Google Scholar 

  15. Cover T and Hart P (1967). Nearest neighbor pattern classification. IEEE Trans Inform Theory 13(1): 21–27

    Article  MATH  Google Scholar 

  16. Dasarathy BV (ed) (1991) Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press

  17. Dempster AP, Laird NM and Rubin DB (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). J Roy Stat Soc B 39: 1–38

    MATH  MathSciNet  Google Scholar 

  18. Devroye L, Gyorfi L, Lugosi G (1996) A probabilistic theory of pattern recognition. Springer, New York. ISBN 0-387-94618-7

  19. Dhillon IS, Guan Y, Kulis B (2004) Kernel k-means: spectral clustering and normalized cuts. KDD 2004, pp 551–556

  20. Dietterich TG (1997). Machine learning: Four current directions. AI Mag 18(4): 97–136

    Google Scholar 

  21. Domingos P (1999) MetaCost: A general method for making classifiers cost-sensitive. In: Proceedings of the fifth international conference on knowledge discovery and data mining, pp 155–164

  22. Domingos P and Pazzani M (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29: 103–130

    Article  MATH  Google Scholar 

  23. Fix E, Hodges JL, Jr (1951) Discriminatory analysis, nonparametric discrimination. USAF School of Aviation Medicine, Randolph Field, Tex., Project 21-49-004, Rept. 4, Contract AF41(128)-31, February 1951

  24. Freund Y and Schapire RE (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1): 119–139

    Article  MATH  MathSciNet  Google Scholar 

  25. Friedman JH, Bentley JL, Finkel RA (1977) An algorithm for finding best matches in logarithmic time. ACM Trans. Math. Software 3, 209. Also available as Stanford Linear Accelerator Center Rep. SIX-PUB-1549, February 1975

  26. Friedman JH, Kohavi R, Yun Y (1996) Lazy decision trees. In: Proceedings of the thirteenth national conference on artificial intelligence, San Francisco, CA. AAAI Press/MIT Press, pp. 717–724

  27. Friedman N, Geiger D and Goldszmidt M (1997). Bayesian network classifiers. Mach Learn 29: 131–163

    Article  MATH  Google Scholar 

  28. Fung G and Stoeckel J (2007). SVM feature selection for classification of SPECT images of Alzheimer’s disease using spatial information. Knowl Inf Syst 11(2): 243–258

    Article  Google Scholar 

  29. Gates GW (1972). The reduced nearest neighbor rule. IEEE Trans Inform Theory 18: 431–433

    Article  Google Scholar 

  30. Golub GH, Van Loan CF (1983) Matrix computations. The Johns Hopkins University Press

  31. Gondek D and Hofmann T (2007). Non-redundant data clustering. Knowl Inf Syst 12(1): 1–24

    Article  Google Scholar 

  32. Han E (1999) Text categorization using weight adjusted k-nearest neighbor classification. PhD thesis, University of Minnesota, October 1999

  33. Hand DJ and Yu K (2001). Idiots Bayes—not so stupid after all?. Int Stat Rev 69: 385–398

    Article  MATH  Google Scholar 

  34. Gray RM and Neuhoff DL (1998). Quantization. IEEE Trans Inform Theory 44(6): 2325–2384

    Article  MATH  MathSciNet  Google Scholar 

  35. Hart P (1968). The condensed nearest neighbor rule. IEEE Trans Inform Theory 14: 515–516

    Article  Google Scholar 

  36. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of ACM SIGMOD international conference on management of data, pp 1–12

  37. Hastie T and Tibshirani R (1996). Discriminant adaptive nearest neighbor classification. IEEE Trans Pattern Anal Mach Intell 18(6): 607–616

    Article  Google Scholar 

  38. Friedman J, Hastie T and Tibshirani R (2000). Additive logistic regression: a statistical view of boosting with discussions. Ann Stat 28(2): 337–407

    Article  MATH  MathSciNet  Google Scholar 

  39. Herbrich R, Graepel T, Obermayer K (2000) Rank boundaries for ordinal regression. Adv Mar Classif pp 115–132

  40. Hu T and Sung SY (2006). Finding centroid clusterings with entropy-based criteria. Knowl Inf Syst 10(4): 505–514

    Article  Google Scholar 

  41. Hunt EB, Marin J and Stone PJ (1966). Experiments in induction. Academic Press, New York

    Google Scholar 

  42. Inokuchi A, Washio T and Motoda H (2005). General framework for mining frequent subgraphs from labeled graphs. Fundament Inform 66(1-2): 53–82

    MATH  MathSciNet  Google Scholar 

  43. Jain AK and Dubes RC (1988). Algorithms for clustering data. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  44. Jin R, Goswami A and Agrawal G (2006). Fast and exact out-of-core and distributed k-means clustering. Knowl Inf Syst 10(1): 17–40

    Article  Google Scholar 

  45. Kobayashi M and Aono M (2006). Exploring overlapping clusters using dynamic re-scaling and sampling. Knowl Inf Syst 10(3): 295–313

    Article  Google Scholar 

  46. Koga H, Ishibashi T and Watanabe T (2007). Fast agglomerative hierarchical clustering algorithm using Locality-Sensitive Hashing. Knowl Inf Syst 12(1): 25–53

    Article  Google Scholar 

  47. Kukar M (2006). Quality assessment of individual classifications in machine learning and data mining. Knowl Inf Syst 9(3): 364–384

    Article  Google Scholar 

  48. Kuramochi M and Karypis G (2005). Gene Classification using Expression Profiles: A Feasibility Study. Int J Artif Intell Tools 14(4): 641–660

    Article  Google Scholar 

  49. Langville AN and Meyer CD (2006). Google’s PageRank and beyond: the science of search engine rankings. Princeton University Press, Princeton

    MATH  Google Scholar 

  50. Leung CW-k, Chan SC-f and Chung F-L (2006). A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowl Inf Syst 10(3): 357–381

    Article  Google Scholar 

  51. Li T, Zhu S and Ogihara M (2006). Using discriminant analysis for multi-class classification: an experimental investigation. Knowl Inf Syst 10(4): 453–472

    Article  Google Scholar 

  52. Liu B (2007). Web data mining: exploring hyperlinks, contents and usage Data. Springer, Heidelberg

    MATH  Google Scholar 

  53. Lloyd SP (1957) Least squares quantization in PCM. Unpublished Bell Lab. Tech. Note, portions presented at the Institute of Mathematical Statistics Meeting Atlantic City, NJ, September 1957. Also, IEEE Trans Inform Theory (Special Issue on Quantization), vol IT-28, pp 129–137, March 1982

  54. Leung CK-S, Khan QI, Li Z and Hoque T (2007). CanTree: a canonical-order tree for incremental frequent-pattern mining. Knowl Inf Syst 11(3): 287–311

    Article  Google Scholar 

  55. McLachlan GJ (1987). On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture.. Appl Stat 36: 318–324

    Article  Google Scholar 

  56. McLachlan GJ and Krishnan T (1997). The EM algorithm and extensions. Wiley, New York

    MATH  Google Scholar 

  57. McLachlan GJ and Peel D (2000). Finite Mixture Models. Wiley, New York

    MATH  Google Scholar 

  58. Messenger RC and Mandell ML (1972). A model search technique for predictive nominal scale multivariate analysis. J Am Stat Assoc 67: 768–772

    Article  Google Scholar 

  59. Morishita S, Sese J (2000) Traversing lattice itemset with statistical metric pruning. In: Proceedings of PODS’00, pp 226–236

  60. Olshen R (2001). A conversation with Leo Breiman. Stat Sci 16(2): 184–198

    Article  MATH  MathSciNet  Google Scholar 

  61. Page L, Brin S, Motwami R, Winograd T (1999) The PageRank citation ranking: bringing order to the Web. Technical Report 1999–0120, Computer Science Department, Stanford University

  62. Quinlan JR (1979) Discovering rules by induction from large collections of examples. In: Michie D (ed), Expert systems in the micro electronic age. Edinburgh University Press, Edinburgh

  63. Quinlan R (1989) Unknown attribute values in induction. In: Proceedings of the sixth international workshop on machine learning, pp. 164–168

  64. Quinlan JR (1993). C4.5: Programs for machine learning. Morgan Kaufmann Publishers, San Mateo

    Google Scholar 

  65. Reyzin L, Schapire RE (2006) How boosting the margin can also boost classifier complexity. In: Proceedings of the 23rd international conference on machine learning. Pittsburgh, PA, pp. 753–760

  66. Ridgeway G, Madigan D and Richardson T (1998). Interpretable boosted naive Bayes classification. In: Agrawal, R, Stolorz, P, and Piatetsky-Shapiro, G (eds) Proceedings of the fourth international conference on knowledge discovery and data mining., pp 101–104. AAAI Press, Menlo Park

    Google Scholar 

  67. Schapire RE (1990). The strength of weak learnability. Mach Learn 5(2): 197–227

    Google Scholar 

  68. Schapire RE, Freund Y, Bartlett P and Lee WS (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. Ann Stat 26(5): 1651–1686

    Article  MATH  MathSciNet  Google Scholar 

  69. Schapire RE and Singer Y (1999). Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3): 297–336

    Article  MATH  Google Scholar 

  70. Scholkopf B, Smola AJ (2002) Learning with kernels. MIT Press

  71. Seidl T and Kriegel H (1998). Optimal multi-step k-nearest neighbor search. In: Tiwary, A and Franklin, M (eds) Proceedings of the 1998 ACM SIGMOD international conference on management of data, Seattle, Washington, United States, 1–4 June, 1998, pp 154–165. ACM Press, New York

    Chapter  Google Scholar 

  72. Srikant R, Agrawal R (1995) Mining generalized association rules. In: Proceedings of the 21st VLDB conference. pp. 407–419

  73. Steinbach M, Karypis G, Kumar V (2000) A comparison of document clustering techniques. In: Proceedings of the KDD Workshop on Text Mining

  74. Steinbach M and Kumar V (2007). Generalizing the notion of confidence. Knowl Inf Syst 12(3): 279–299

    Article  Google Scholar 

  75. Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Pearson Addison-Wesley

  76. Tao D, Li X, Wu X, Hu W and Maybank SJ (2007). Supervised tensor learning. Knowl Inf Syst 13(1): 1–42

    Article  Google Scholar 

  77. Thabtah FA, Cowling PI and Peng Y (2006). Multiple labels associative classification. Knowl Inf Syst 9(1): 109–129

    Article  Google Scholar 

  78. Ting KM (2002). An instance-weighting method to induce cost-sensitive trees. IEEE Trans Knowl Data Eng 14: 659–665

    Article  Google Scholar 

  79. Toussaint GT (2002) Proximity graphs for nearest neighbor decision rules: recent progress. In: Interface-2002, 34th symposium on computing and statistics (theme: Geoscience and Remote Sensing). Ritz-Carlton Hotel, Montreal, Canada, 17–20 April, 2002

  80. Toussaint GT (2002) Open problems in geometric methods for instance-based learning. JCDCG 273–283

  81. Tsang IW, Kwok JT and Cheung P-M (2005). Core vector machines: Fast SVM training on very large data sets. J Mach Learn Res 6: 363–392

    MathSciNet  Google Scholar 

  82. Uno T, Asai T, Uchida Y, Arimura H (2004) An efficient algorithm for enumerating frequent closed patterns in transaction databases. In: Proc. of the 7th international conference on discovery science. LNAI vol 3245, Springe, Heidelberg, pp 16–30

  83. Vapnik V (1995). The nature of statistical learning theory. Springer, New York

    MATH  Google Scholar 

  84. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. pages 511–518, Kauai, HI

  85. Washio T, Nakanishi K, Motoda H (2005) Association rules based on levelwise subspace clustering. In: Proceedings. of 9th European conference on principles and practice of knowledge discovery in databases. LNAI, vol 3721, pp. 692–700 Springer, Heidelberg

  86. Wasserman S and Raust K (1994). Social network analysis. Cambridge University Press, Cambridge

    Google Scholar 

  87. Wettschereck D, Aha D and Mohri T (1997). A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif Intell Rev 11: 273–314

    Article  Google Scholar 

  88. Wilson DL (1972). Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans Syst Man Cyberne 2: 408–420

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  90. Yan X, Han J (2002) gSpan: Graph-based substructure pattern mining. In: Proceedings of ICDM’02, pp 721–724

  91. Yu PS, Li X, Liu B (2005) Adding the temporal dimension to search—a case study in publication search. In: Proceedings of Web Intelligence (WI’05)

  92. Zhang J, Kang D-K, Silvescu A and Honavar V (2006). Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data. Knowl Inf Syst 9(2): 157–179

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of computer Science, University of Vermont, Burlington, VT, USA

    Xindong Wu

  2. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA

    Vipin Kumar

  3. Rulequest Research Pty Ltd, St Ives, NSW, Australia

    J. Ross Quinlan

  4. Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, 78712, USA

    Joydeep Ghosh

  5. Department of Computer Science, Hong Kong University of Science and Technology, Honkong, China

    Qiang Yang

  6. AFOSR/AOARD and Osaka University, 7-23-17 Roppongi, Minato-ku, Tokyo, 106-0032, Japan

    Hiroshi Motoda

  7. Department of Mathematics, The University of Queensland, Brisbane, Australia

    Geoffrey J. McLachlan

  8. School of Medicine, Griffith University, Brisbane, Australia

    Angus Ng

  9. Department of Computer Science, University of Illinois at Chicago, Chicago, IL, 60607, USA

    Bing Liu

  10. IBM T. J. Watson Research Center, Hawthorne, NY, 10532, USA

    Philip S. Yu

  11. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Zhi-Hua Zhou

  12. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    Michael Steinbach

  13. Department of Mathematics, Imperial College, London, UK

    David J. Hand

  14. Salford Systems, San Diego, CA, 92123, USA

    Dan Steinberg

Authors
  1. Xindong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vipin Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Ross Quinlan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joydeep Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hiroshi Motoda
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Geoffrey J. McLachlan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Angus Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Philip S. Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Zhi-Hua Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Michael Steinbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. David J. Hand
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Dan Steinberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xindong Wu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, X., Kumar, V., Ross Quinlan, J. et al. Top 10 algorithms in data mining. Knowl Inf Syst 14, 1–37 (2008). https://doi.org/10.1007/s10115-007-0114-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-007-0114-2

Keywords

Search

Navigation