Adaptive Clustering Techniques and Their Applications

Adaptive Clustering Techniques and Their Applications

Deepthi P. Hudedagaddi (VIT University, India) and B. K. Tripathy (VIT University, India)
Copyright: © 2017 |Pages: 18
DOI: 10.4018/978-1-5225-1776-4.ch015
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With the increasing volume of data, developing techniques to handle it has become the need of the hour. One such efficient technique is clustering. Data clustering is under vigorous development. The goal of clustering is to determine the intrinsic grouping in a set of unlabeled data. Several data clustering algorithms have been developed in this regard. Data is uncertain and vague. Hence uncertain and hybrid based clustering algorithms like fuzzy c means, intuitionistic fuzzy c means, rough c means, rough intuitionistic fuzzy c means are being used. However, with the application and nature of data, clustering algorithms which adapt to the need are being used. These are nothing but the variations in existing techniques to match a particular scenario. The area of adaptive clustering algorithms is unexplored to a very large extent and hence has a large scope of research. Adaptive clustering algorithms are useful in areas where the situations keep on changing. Some of the adaptive fuzzy c means clustering algorithms are detailed in this chapter.
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Learning to read clusters is not something your eyes do naturally. It takes constant practice.

-Bill Cosby



Human beings have been applying clustering techniques subconsciously as per the situation and hence adapting to the environment. This has helped him in understanding or solving real world problems. Clustering is the unsupervised classification of patterns (observations, data items or feature vectors) into groups (or clusters) (Bijuraj, 1993). A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters(Han,2011). Clustering is a challenging field of research in which its potential applications pose their own special requirements and they are

  • Scalability.

  • Ability to deal with different types of attributes.

  • Discovery of clusters with arbitrary shape.

  • Minimal requirements for domain knowledge to determine input parameters.

  • Ability to deal noisy data.

  • Incremental clustering and insensitivity to the order of input records.

  • High dimensionality.

  • Constraint-based clustering.

  • Interpretability and usability.

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