The 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 algorithms are among the most influential in data mining. Each algorithm is described, its impact discussed, and current research reviewed. The algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are key areas in data mining. The paper begins with an introduction to the identification process, which involved nominations from award winners and community voting. The top 10 algorithms were selected through a multi-step process, including nominations, verification, and community voting. The final selection was confirmed through an open vote at the ICDM '06 conference. Section 1 discusses C4.5 and its successors, including decision trees, ruleset classifiers, and See5/C5.0. C4.5 is a decision tree algorithm that can also generate ruleset classifiers. It uses information gain and gain ratio to select attributes and prunes trees to avoid overfitting. See5/C5.0 improves upon C4.5 with better efficiency and new features. Research issues include stable trees and decomposing complex trees. Section 2 describes the k-means algorithm, which partitions data into clusters. It is sensitive to initialization and has limitations in handling non-convex clusters and outliers. It can be improved by using different distance measures or combining with other algorithms. Generalizations include using Bregman divergences and kernel methods. Section 3 covers support vector machines (SVM), which are robust and accurate for classification. SVMs find the maximum margin hyperplane to separate data. They can handle non-linear data using kernel methods and can be extended for regression and ranking tasks. Research issues include understanding SVM theory and scaling to large datasets. Section 4 discusses the Apriori algorithm for finding frequent itemsets and association rules. It uses candidate generation and pruning to reduce the number of candidates. It is simple but can be inefficient for large datasets. Improvements include FP-growth, which eliminates candidate generation. Section 5 describes the EM algorithm for mixture models, which estimates parameters using maximum likelihood. It is used for clustering and estimating densities. The algorithm alternates between expectation and maximization steps. Section 6 introduces PageRank, a search ranking algorithm based on hyperlink structures. It assigns a static rank to web pages based on the number and importance of incoming links. PageRank is used to determine the importance of web pages in search engines.The 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 algorithms are among the most influential in data mining. Each algorithm is described, its impact discussed, and current research reviewed. The algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are key areas in data mining. The paper begins with an introduction to the identification process, which involved nominations from award winners and community voting. The top 10 algorithms were selected through a multi-step process, including nominations, verification, and community voting. The final selection was confirmed through an open vote at the ICDM '06 conference. Section 1 discusses C4.5 and its successors, including decision trees, ruleset classifiers, and See5/C5.0. C4.5 is a decision tree algorithm that can also generate ruleset classifiers. It uses information gain and gain ratio to select attributes and prunes trees to avoid overfitting. See5/C5.0 improves upon C4.5 with better efficiency and new features. Research issues include stable trees and decomposing complex trees. Section 2 describes the k-means algorithm, which partitions data into clusters. It is sensitive to initialization and has limitations in handling non-convex clusters and outliers. It can be improved by using different distance measures or combining with other algorithms. Generalizations include using Bregman divergences and kernel methods. Section 3 covers support vector machines (SVM), which are robust and accurate for classification. SVMs find the maximum margin hyperplane to separate data. They can handle non-linear data using kernel methods and can be extended for regression and ranking tasks. Research issues include understanding SVM theory and scaling to large datasets. Section 4 discusses the Apriori algorithm for finding frequent itemsets and association rules. It uses candidate generation and pruning to reduce the number of candidates. It is simple but can be inefficient for large datasets. Improvements include FP-growth, which eliminates candidate generation. Section 5 describes the EM algorithm for mixture models, which estimates parameters using maximum likelihood. It is used for clustering and estimating densities. The algorithm alternates between expectation and maximization steps. Section 6 introduces PageRank, a search ranking algorithm based on hyperlink structures. It assigns a static rank to web pages based on the number and importance of incoming links. PageRank is used to determine the importance of web pages in search engines.