資料探勘18種候選演算法和十大經典演算法
阿新 • • 發佈：2019-01-10
國際權威的學術組織the IEEE International Conference on Data Mining (ICDM) 2006（香港召開）年12月評選出了資料探勘領域的十大經典演算法。不僅僅是選中的十大演算法，其實參加評選的18種演算法，實際上隨便拿出一種來都可以稱得上是經典演算法，它們在資料探勘領域都產生了極為深遠的影響。
一、資料探勘18種候選演算法
Classification（分類）
==============
#1. C4.5
Quinlan, J. R. 1993. C4.5: Programs for Machine Learning.
Morgan Kaufmann Publishers Inc.
Google Scholar Count in October 2006: 6907
#2. CART
L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and
Regression Trees. Wadsworth, Belmont, CA, 1984.
Google Scholar Count in October 2006: 6078
#3. K Nearest Neighbours (kNN)
Hastie, T. and Tibshirani, R. 1996. Discriminant Adaptive Nearest
Neighbor Classification. IEEE Trans. Pattern
Anal. Mach. Intell. (TPAMI). 18, 6 (Jun. 1996), 607-616.
DOI= http://dx.doi.org/10.1109/34.506411
Google SCholar Count: 183
#4. Naive Bayes
Hand, D.J., Yu, K., 2001. Idiot's Bayes: Not So Stupid After All?
Internat. Statist. Rev. 69, 385-398.
Google Scholar Count in October 2006: 51
Statistical Learning（統計學習）
====================
#5. SVM
Vapnik, V. N. 1995. The Nature of Statistical Learning
Theory. Springer-Verlag New York, Inc.
Google Scholar Count in October 2006: 6441
#6. EM
McLachlan, G. and Peel, D. (2000). Finite Mixture Models.
J. Wiley, New York.
Google Scholar Count in October 2006: 848
Association Analysis（關聯分析）
====================
#7. Apriori
Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining
Association Rules. In Proc. of the 20th Int'l Conference on Very Large
Databases (VLDB '94), Santiago, Chile, September 1994.
http://citeseer.comp.nus.edu.sg/agrawal94fast.html
Google Scholar Count in October 2006: 3639
#8. FP-Tree
Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without
candidate generation. In Proceedings of the 2000 ACM SIGMOD
international Conference on Management of Data (Dallas, Texas, United
States, May 15 - 18, 2000). SIGMOD '00. ACM Press, New York, NY, 1-12.
DOI= http://doi.acm.org/10.1145/342009.335372
Google Scholar Count in October 2006: 1258
Link Mining（連結挖掘）
===========
#9. PageRank
Brin, S. and Page, L. 1998. The anatomy of a large-scale hypertextual
Web search engine. In Proceedings of the Seventh international
Conference on World Wide Web (WWW-7) (Brisbane,
Australia). P. H. Enslow and A. Ellis, Eds. Elsevier Science
Publishers B. V., Amsterdam, The Netherlands, 107-117.
DOI= http://dx.doi.org/10.1016/S0169-7552(98)00110-X
Google Shcolar Count: 2558
#10. HITS
Kleinberg, J. M. 1998. Authoritative sources in a hyperlinked
environment. In Proceedings of the Ninth Annual ACM-SIAM Symposium on
Discrete Algorithms (San Francisco, California, United States, January
25 - 27, 1998). Symposium on Discrete Algorithms. Society for
Industrial and Applied Mathematics, Philadelphia, PA, 668-677.
Google Shcolar Count: 2240
Clustering（聚類）
==========
#11. K-Means
MacQueen, J. B., Some methods for classification and analysis of
multivariate observations, in Proc. 5th Berkeley Symp. Mathematical
Statistics and Probability, 1967, pp. 281-297.
Google Scholar Count in October 2006: 1579
#12. BIRCH
Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient
data clustering method for very large databases. In Proceedings of the
1996 ACM SIGMOD international Conference on Management of Data
(Montreal, Quebec, Canada, June 04 - 06, 1996). J. Widom, Ed.
SIGMOD '96. ACM Press, New York, NY, 103-114.
DOI= http://doi.acm.org/10.1145/233269.233324
Google Scholar Count in October 2006: 853
Bagging and Boosting（袋裝與推進）
====================
#13. AdaBoost
Freund, Y. and Schapire, R. E. 1997. A decision-theoretic
generalization of on-line learning and an application to
boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), 119-139.
DOI= http://dx.doi.org/10.1006/jcss.1997.1504
Google Scholar Count in October 2006: 1576
Sequential Patterns（序列模式）
===================
#14. GSP
Srikant, R. and Agrawal, R. 1996. Mining Sequential Patterns:
Generalizations and Performance Improvements. In Proceedings of the
5th international Conference on Extending Database Technology:
Advances in Database Technology (March 25 - 29, 1996). P. M. Apers,
M. Bouzeghoub, and G. Gardarin, Eds. Lecture Notes In Computer
Science, vol. 1057. Springer-Verlag, London, 3-17.
Google Scholar Count in October 2006: 596
#15. PrefixSpan
J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and
M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by
Prefix-Projected Pattern Growth. In Proceedings of the 17th
international Conference on Data Engineering (April 02 - 06,
2001). ICDE '01. IEEE Computer Society, Washington, DC.
Google Scholar Count in October 2006: 248
Integrated Mining（整合挖掘）
=================
#16. CBA
Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and
association rule mining. KDD-98, 1998, pp. 80-86.
http://citeseer.comp.nus.edu.sg/liu98integrating.html
Google Scholar Count in October 2006: 436
Rough Sets（粗糙集）
==========
#17. Finding reduct
Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about
Data, Kluwer Academic Publishers, Norwell, MA, 1992
Google Scholar Count in October 2006: 329
Graph Mining（圖挖掘）
============
#18. gSpan
Yan, X. and Han, J. 2002. gSpan: Graph-Based Substructure Pattern
Mining. In Proceedings of the 2002 IEEE International Conference on
Data Mining (ICDM '02) (December 09 - 12, 2002). IEEE Computer
Society, Washington, DC.
Google Scholar Count in October 2006: 155
二、資料探勘十大經典演算法
十大經典演算法是：C4.5、k-Means、 SVM、 Apriori、 EM、 PageRank、AdaBoost、 kNN、Naive Bayes、and CART。每個演算法的詳細介紹在以下的部落格中書寫（待寫）。
三、10大演算法評選出來的三大步驟
1、提名（Nominations）
在2006年9月，我們邀請了ACM KDD創新大獎(Innovaction Award)和IEEE ICDM研究貢獻獎(Research Contributions Award)的獲獎者們來參與資料探勘10大演算法的選舉，每人提名10種他認為最重要的演算法。
除一人未參與外，其他獲獎者均給出了演算法的提名。
每個提名中均需給出以下資訊：（1）演算法名稱（2）提名理由摘要（3）演算法的代表性論文。
每個提名演算法都應該被相關領域的研究者們廣泛引用和使用，每位提名者給出的同類演算法應該是資料探勘重要應用領域的代表。
2、稽核（Verification）
在2006年10月，我們通過Google Scholar對每個提名演算法的引用情況進行了稽核，從候選名單中刪除了低於50篇論文引用的演算法。
最終剩下18種提名演算法通過了稽核，它們分屬10類資料探勘主題。
3、投票（Voting）
我們邀請了更多的專業人士來從這些候選演算法中投票選出10大演算法，他們包括（1）KDD-06、ICDM’06和SDM’06的程式委員會成員（Program Committee members） （2）ACM KDD創新大獎和IEEE ICDM研究貢獻獎的獲獎者們
根據投票排名篩選出10大演算法（如果票數相同，則按字母順序進行排名）。
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