paper

Top 10 algorithms in data mining

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📜 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, k NN, 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.

✨ Summary

Influence

  • The paper formalized an expert-voted reference list of ten widely used algorithms—C4.5, k-means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART—and connected them to the principal data-mining areas of classification, clustering, statistical learning, association analysis, and link mining. (researchgate.net)
  • Subsequent research used the list as a baseline for measuring algorithm prominence. A quantitative meta-study explicitly revisited the 2007 selection using approximately 54,000 research abstracts from 2007–2016, comparing the original algorithm-centered view with broader trends in machine-learning topics. (researchgate.net)
  • A 2018 study applied the list to 5,212 ACL conference papers and 6,341 algorithm-mention sentences to quantify influence within natural-language processing. It found that SVM had the broadest influence by number of papers and mentions, while PageRank had the highest average mention frequency in that corpus. (researchgate.net)
  • The list has also been reproduced in educational data-mining material as a canonical table linking each algorithm to its category and instructional coverage, indicating continued pedagogical use. (sciencedirect.com)

Overall, the paper’s principal impact was to provide a concise, widely reused taxonomy of foundational data-mining algorithms that later studies adopted for bibliometric analysis, domain-specific usage analysis, and teaching.