Exploring the Unknown Nature of Data: Cluster Analysis and Applications
Olivas, Emilio Soria et. al.
To classify objects based on their features and characteristics is one of the most important and primitive activities of human beings. The task becomes even more challenging when there is no ground truth available. Cluster analysis allows new opportunities in exploring the unknown nature of data through its aim to separate a finite data set, with little or no prior information, into a finite and discrete set of "natural," hidden data structures. Here, the authors introduce and discuss clustering algorithms that are related to machine learning and computational intelligence, particularly those based on neural networks. Neural networks are well known for their good learning capabilities, adaptation, ease of implementation, parallelization, speed, and flexibility, and they have demonstrated many successful applications in cluster analysis. The applications of cluster analysis in real world problems are also illustrated. Portions of the chapter are taken from Xu and Wunsch (2008).
R. Xu and D. C. Wunsch, "Exploring the Unknown Nature of Data: Cluster Analysis and Applications," Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques, pp. 1-27, IGI Global, Jan 2009.
The definitive version is available at https://doi.org/10.4018/978-1-60566-766-9.ch001
Electrical and Computer Engineering
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