Top 10 algorithms in data mining

By Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus F. M. Ng, B. Liu, Philip S. Yu and 4 more. 2007

5,999 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Algorithms & Complexity

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Cynthia Rudin
2. Philip S. Yu
3. Berk Ustun
4. Francisco Herrera
5. Qiang Yang
6. Abhradeep Thakurta
7. Alberto Fernández
8. Emilio Carrizosa

In Computer Vision and Pattern Recognition

1. Xuelong Li
2. Dacheng Tao
3. Qiang Yang
4. Cynthia Rudin
5. Berk Ustun
6. Emilio Carrizosa
7. Dolores Romero Morales
8. Shichao Zhang

In Information Systems

1. Francisco Herrera
2. Philip S. Yu
3. Alberto Fernández
4. Berk Ustun
5. Qiang Yang
6. Tat-seng Chua
7. Chengqi Zhang
8. Yongfeng Zhang

In Algorithms & Complexity

1. Ravi Kumar
2. Berk Ustun
3. Benjamin Moseley
4. Xuelong Li
5. Dolores Romero Morales
6. Emilio Carrizosa
7. Qiang Yang
8. Abhradeep Thakurta

Citations in the corpus

• Interpretable classification models for recidivism prediction (2015, methodology): “We compared the performance of models from eight different classification methods, including those previously used for recidivism prediction (see Section 2.2) or that ranked among the “top 10 algorithms in data mining” (Wu et al. 2008).”
• Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, background): “Supervised Classi cation, (Apte 2003, Hand et al. 2001, Wu et al. 2007), based on large datasets, plays an important role in many industries, with notable examples being credit scoring (Baesens et al. 2003), fraud detection (Cecchini et al. 2010), customer targeting (Archak et al. 2011, Cui et al.…”
• An Adaptive Ensemble Machine Learning Model for Intrusion Detection (2019, methodology): “The adaptive ensemble learning model designed in this paper chooses common machine learning algorithms such as decision tree, SVM(support vector machines), logical regression, kNN(k-nearest neighbors) [27], Adaboost, random forest and deep neural network as alternative classifiers.”
• An up-to-date comparison of state-of-the-art classification algorithms (2017, methodology): “The selected classification algorithms in this work are the most popular ones, including almost all the classifiers from the top-10 data mining algorithms, reported in Wu et al. (2008) .”
• Data mining combined to the multicriteria decision analysis for the improvement of road safety: case of France (2019, background): “These data mining tools are particularly effective in extracting significant information and extracting exactly the useful knowledge, from large amounts of data [30].”
• A Survey on Transfer Learning (2010, background): “ATA mining and machine learning technologies have already achieved significant success in many knowledge engineering areas including classification, regression, and clustering (e.g., [1], [2]).”
• Local Search Methods for k-Means with Outliers (2017, neutral): “Using empirical evaluation on both synthetic and large-scale real-world data, we demonstrate that the algorithm dominates recently proposed heuristic approaches for the problem.”
• Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “During the 2006 IEEE International Conference on Data Mining (ICDM), the ten most influential data mining algorithms were identified based on rigorous election [193].”
• A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches (2012, methodology): “AdaBoost [41] is the most representative algorithm in this family, it was the first applicable approach of Boosting, and it has been appointed as one of the top ten data mining algorithms [94].”