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A Consolidate Review of the recent challenges in Clustering Uniform Effect

Nagul Shaik*, R. Kiran Kumar**

* Research Scholar, Department of Computer Science, Krishna University, Machilipatnam, India.

** Senior Assistant Professor, Department of Computer Science, Krishna University, Machilipatnam, India.

Periodicity:September - November'2017
DOI : https://doi.org/10.26634/jit.6.4.13848

Abstract

The applications of data mining in the recent decade are increased exponentially. The devices through which data is collected and analyzed are different from the previous data collected. One of the data emerged in the recent years for knowledge discovery is class imbalance data. Class imbalance data can be defined as the data with extreme imbalance in the ratio of the class instances. In this paper, the authors have presented different scenarios of clustering algorithms for tackling such type of data; especially k-means algorithm towards class imbalance data. The survey provided a shortcoming of the k-means algorithm towards class imbalance data known as 'uniform effect'. The different causes and reasons for such behavior are analyzed with different benchmark imbalance data with different evaluation criterias.

Keywords

Clustering, Class Imbalance, Uniform Effect, Learning Strategies

How to Cite this Article?

Shaik, N., and Kumar, K. B. (2017). A Consolidate Review of the recent challenges in Clustering Uniform Effect. i-manager’s Journal on Information Technology, 6(4), 30-37. https://doi.org/10.26634/jit.6.4.13848

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