Estivill-Castro, Vladimir. Why so many clustering algorithms – A Position Paper. ACM SIGKDD Explorations Newsletter. 20 June 2002, 4 (1): 65–75. S2CID 7329935. doi:10.1145/568574.568575.
Defays, D. An efficient algorithm for a complete link method. The Computer Journal (British Computer Society). 1977, 20 (4): 364–366. doi:10.1093/comjnl/20.4.364.
Ester, Martin; Kriegel, Hans-Peter; Sander, Jörg; Xu, Xiaowei. A density-based algorithm for discovering clusters in large spatial databases with noise. Simoudis, Evangelos; Han, Jiawei; Fayyad, Usama M. (編). Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI Press: 226–231. 1996. ISBN 1-57735-004-9.
Ankerst, Mihael; Breunig, Markus M.; Kriegel, Hans-Peter; Sander, Jörg. OPTICS: Ordering Points To Identify the Clustering Structure. ACM SIGMOD international conference on Management of data. ACM Press: 49–60. 1999. CiteSeerX 10.1.1.129.6542.
Pfitzner, Darius; Leibbrandt, Richard; Powers, David. Characterization and evaluation of similarity measures for pairs of clusterings. Knowledge and Information Systems (Springer). 2009, 19 (3): 361–394. S2CID 6935380. doi:10.1007/s10115-008-0150-6.
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For spectral clustering :
Jianbo Shi and Jitendra Malik, "Normalized Cuts and Image Segmentation", IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888-905, August 2000. Available on Jitendra Malik's homepage (頁面存檔備份,存於網際網路檔案館)
Marina Meila and Jianbo Shi, "Learning Segmentation with Random Walk", Neural Information Processing Systems, NIPS, 2001. Available from Jianbo Shi's homepage (頁面存檔備份,存於網際網路檔案館)
For estimating number of clusters:
Can, F., Ozkarahan, E. A. (1990) "Concepts and effectiveness of the cover coefficient-based clustering methodology for text databases." ACM Transactions on Database Systems. 15 (4) 483-517.
for another presentation of hierarchical, k-means and fuzzy c-means see this introduction to clustering (頁面存檔備份,存於網際網路檔案館). Also has an explanation on mixture of Gaussians.
YALE (Yet Another Learning Environment): freely available open-source software for data pre-processing, knowledge discovery, data mining, machine learning, visualization, etc. also including a plugin for clustering, fully integrating Weka, easily extendible, and featuring a graphical user interface as well as a XML-based scripting language for data mining;
mixmod (頁面存檔備份,存於網際網路檔案館) : Model Based Cluster And Discriminant Analysis. Code in C++, interface with Matlab and Scilab
LingPipe Clustering Tutorial (頁面存檔備份,存於網際網路檔案館) Tutorial for doing complete- and single-link clustering using LingPipe, a Java text data mining package distributed with source.
Weka : Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Tanagra (頁面存檔備份,存於網際網路檔案館) : a free data mining software including several clustering algorithms such as K-MEANS, SOM, Clustering Tree, HAC and more.
Cluster : Open source clustering software. The routines are available in the form of a C clustering library, an extension module to Python, a module to Perl.