Curated by: Ian Davidson
Cluster analysis or clustering aims to take a collection of objects and divide them into a number of different groups such that instances in the same group (cluster) are similar to each other and dis-similar to those in other groups/clusters. It is extensively used in many domains including image analysis, information retrieval and bioinformatics. Clustering is traditionally inherently exploratory in that it takes no human guidance and aims to uncover the underlying structure in the data. Recent innovations include adding supervision (semi-supervised clustering), constraints (constrained clustering) and extensions to handle complex data such as graphs, evolving data and multi-view data.
The survey of classic methods is given in  with a perspective on challenge and directions given in . A talk based on  is freely available: http://videolectures.net/ecmlpkdd08_jain_dcyb/?q=anil%20jain
Lesson 2 of the this MOOC covers many traditional clustering methods https://www.class-central.com/mooc/1848/udacity-machine-learning-unsupervised-learning.
 Jain, Anil K., M. Narasimha Murty, and Patrick J. Flynn. “Data clustering: a review.” ACM computing surveys (CSUR) 31.3 (1999): 264-323.
 Jain, Anil K. “Data clustering: 50 years beyond K-means.” Pattern recognition letters 31.8 (2010): 651-666.
Related KDD2016 Papers
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Author(s): Jianhua Yin*, Tsinghua University; Jianyong Wang,
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|Efficient Frequent Directions Algorithm for Sparse Matrices|
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|AnyDBC: An Efficient Anytime Density-based Clustering Algorithm for Very Large Complex Datasets|
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Author(s): Leilei Sun*, Dalian University of Technolog; Chuanren Liu, Drexel University; Chonghui Guo, ; Hui Xiong, Rutgers; Yanming Xie,
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Author(s): Xiaoqian Wang, Univ. of Texas at Arlington; Feiping Nie, University of Texas at Arlington; Heng Huang*, Univ. of Texas at Arlington
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Author(s): Hongfu Liu*, Northeastern University; Ming Shao, Northeastern University; Sheng Li, Northeastern University; Yun Fu, Northeastern University