Topic · Data Management and Algorithms
Most cited papers in Data Management and Algorithms
What are the most cited papers in Data Management and Algorithms?
The most cited paper in Data Management and Algorithms is "Silhouettes: A graphical aid to the interpretation and validation of cluster analysis" (Peter J. Rousseeuw, 1987), with 21,780 citations. Next come "A density-based algorithm for discovering clusters in large spatial Databases with Noise" (19,125) and "The WEKA data mining software" (17,902).
Showing 50 of 99 papers. Every paper with more than 1,751 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Silhouettes: A graphical aid to the interpretation and validation of cluster analysis Peter J. Rousseeuw · Journal of Computational and Applied Mathematics | Nov 1987 | 21,780 |
| 2 | A density-based algorithm for discovering clusters in large spatial Databases with Noise Martin Ester, Hans‐Peter Kriegel, Jörg Sander et al. | 1996 | 19,125 |
| 3 | The WEKA data mining software Mark Michael Hall, Eibe Frank, Geoffrey Holmes et al. · ACM SIGKDD Explorations Newsletter | 16 Nov 2009 | 17,902 |
| 4 | Data clustering Anil Kumar Jain, Musti Narasimha Murty, Patrick J. Flynn · ACM Computing Surveys | Sep 1999 | 13,296 |
| 5 | A Formal Basis for the Heuristic Determination of Minimum Cost Paths Peter Hart, Nils J. Nilsson, Bertram Raphael · IEEE Transactions on Systems Science and Cybernetics | 1968 | 12,723 |
| 6 | Rough sets Zdzis aw Pawlak · International Journal of Parallel Programming | Oct 1982 | 12,304 |
| 7 | Ant system: optimization by a colony of cooperating agents Marco Dorigo, Vittorio Maniezzo, A. Colorni · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) | Feb 1996 | 12,083 |
| 8 | Network Flows: Theory, Algorithms, and Applications. David Keith Smith, Ravindra K. Ahuja, Thomas L. Magnanti et al. · Journal of the Operational Research Society | Nov 1994 | 8,169 |
| 9 | Algorithms for Clustering Data Warren S. Sarle, Anil K. Jain, Richard C. Dubes · Technometrics | May 1990 | 7,829 |
| 10 | Multidimensional binary search trees used for associative searching Jon Louis Bentley · Communications of the ACM | Sep 1975 | 7,627 |
| 11 | A vector space model for automatic indexing Gerard Salton, Anita M.-Y. Wong, Chul‐Su Yang · Communications of the ACM | Nov 1975 | 7,521 |
| 12 | FCM: The fuzzy c-means clustering algorithm James C. Bezdek, Robert Ehrlich, William E. Full · Computers & Geosciences | 1984 | 6,688 |
| 13 | k-means++: the advantages of careful seeding David Arthur, Sergei Vassilvitskii | 7 Jan 2007 | 6,293 |
| 14 | An efficient k-means clustering algorithm: analysis and implementation Tapas Kanungo, David M. Mount, Nathan S. Netanyahu et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | Jul 2002 | 5,724 |
| 15 | Cumulated gain-based evaluation of IR techniques Kalervo Järvelin, Jaana Kekäläinen · ACM Transactions on Information Systems | Oct 2002 | 4,810 |
| 16 | The entity-relationship model—toward a unified view of data Peter Pin-Shan Chen · ACM Transactions on Database Systems | Mar 1976 | 4,682 |
| 17 | The Earth Mover's Distance as a Metric for Image Retrieval Yossi Rubner, Carlo Tomasi, Leonidas J. Guibas · International Journal of Computer Vision | Nov 2000 | 4,659 |
| 18 | Multiple Comparisons Using Rank Sums Olive Jean Dunn · Technometrics | Aug 1964 | 4,644 |
| 19 | Citizens as sensors: the world of volunteered geography Michael Frank Goodchild · GeoJournal | 19 Nov 2007 | 4,543 |
| 20 | Empirical Analysis of Predictive Algorithms for Collaborative Filtering John S. Breese, David E. Heckerman, Carl Myers Kadie · arXiv (Cornell University) | 30 Jan 2013 | 4,504 |
| 21 | Voronoi diagrams—a survey of a fundamental geometric data structure Franz Aurenhammer · ACM Computing Surveys | Sep 1991 | 4,387 |
| 22 | Approximate nearest neighbors Piotr Indyk, Rajeev Motwani | 1998 | 4,253 |
| 23 | The R*-tree: an efficient and robust access method for points and rectangles Norbert Beckmann, Hans‐Peter Kriegel, Ralf Schneider et al. | May 1990 | 4,188 |
| 24 | Probabilistic Latent Semantic Indexing Thomas Frank Hofmann · ACM SIGIR Forum | 2 Aug 2017 | 4,050 |
| 25 | BIRCH Tian Yao Zhang, Raghu Ramakrishnan, Miron Livny | 1996 | 3,923 |
| 26 | An Examination of Procedures for Determining the Number of Clusters in a Data Set Glenn W. Milligan, Martha C. Cooper · Psychometrika | Jun 1985 | 3,910 |
| 27 | The global k-means clustering algorithm Aristidis C. Likas, Nikos Vlassis, Jakob J. Verbeek · Pattern Recognition | 28 Oct 2002 | 3,786 |
| 28 | A Bayesian Method for the Induction of Probabilistic Networks from Data Gregory F. Cooper, Edward H. Herskovits · Machine Learning | Oct 1992 | 3,533 |
| 29 | Rough sets Zdzisław Pawlak, Jerzy W. Grzymala‐Busse, Roman Słowiński et al. · Communications of the ACM | Nov 1995 | 3,212 |
| 30 | Recommender systems survey Jesús Bobadilla, Fernando Ortega, Antonio Hernando et al. · Knowledge-Based Systems | 6 Apr 2013 | 3,172 |
| 31 | Fundamentals of Database Systems Ramez A. Elmasri, Shamkant B. Navathe | 1989 | 3,148 |
| 32 | Similarity Search in High Dimensions via Hashing Aristides Gionis, Piotr Indyk, Rajeev Motwani | 7 Sep 1999 | 3,094 |
| 33 | A Survey of Clustering Data Mining Techniques Pavel Berkhin | Feb 2006 | 2,833 |
| 34 | An Algorithm for Finding Best Matches in Logarithmic Expected Time Jerome H. Friedman, Jon Louis Bentley, Raphael A. Finkel · ACM Transactions on Mathematical Software | Sep 1977 | 2,828 |
| 35 | DBSCAN Revisited, Revisited Erich Schubert, Jörg Sander, Martin Ester et al. · ACM Transactions on Database Systems | 31 Jul 2017 | 2,772 |
| 36 | On Clustering Validation Techniques Maria Halkidi, Yannis Batistakis, Michalis Vazirgiannis · Journal of Intelligent Information Systems | Dec 2001 | 2,679 |
| 37 | Finding the K Shortest Loopless Paths in a Network Jin Y. Yen · Management Science | Jul 1971 | 2,628 |
| 38 | FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION Marius Muja, David G. Lowe | 2009 | 2,616 |
| 39 | Density-Based Clustering Based on Hierarchical Density Estimates Ricardo J. G. B. Campello, Davoud Moulavi, Jörg Sander · Lecture notes in computer science | 2013 | 2,570 |
| 40 | How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis Chris Fraley · The Computer Journal | Aug 1998 | 2,566 |
| 41 | Modelling Spatial Patterns Brian David Ripley · Journal of the Royal Statistical Society Series B (Statistical Methodology) | 1977 | 2,559 |
| 42 | Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values Zhexue Huang · Data Mining and Knowledge Discovery | Sep 1998 | 2,525 |
| 43 | Data integration Maurizio Lenzerini | 3 Jun 2002 | 2,519 |
| 44 | Models and issues in data stream systems Brian Babcock, Shivnath Babu, Mayur Datar et al. | 3 Jun 2002 | 2,517 |
| 45 | Unsupervised Learning by Probabilistic Latent Semantic Analysis Thomas Frank Hofmann · Machine Learning | 2001 | 2,479 |
| 46 | Consistency in networks of relations Alan K. Mackworth · Artificial Intelligence | Feb 1977 | 2,463 |
| 47 | A Comparison of Document Clustering Techniques Michael S. Steinbach, George Karypis, Vipin Kumar | 23 May 2000 | 2,453 |
| 48 | An Introduction to MCMC for Machine Learning Christophe Andrieu, Nando de Freitas, Arnaud Doucet et al. · Machine Learning | 2003 | 2,448 |
| 49 | An overview of data warehousing and OLAP technology Surajit Chaudhuri, Umeshwar Dayal · ACM SIGMOD Record | Mar 1997 | 2,423 |
| 50 | Automatic subspace clustering of high dimensional data for data mining applications Rakesh Agrawal, Johannes E. Gehrke, Dimitrios Gunopulos et al. | Jun 1998 | 2,402 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.