Performance Analysis of Clustering Algorithms in Outlier Detection Based on Statistical Models and Spatial Proximity

Provided by: International Journal of Computer Science and Information Technologies
Topic: Data Management
Format: PDF
In this paper, the authors present the analysis of leader-follower, k-means and k-medians clustering algorithms in outlier detection based on some statistical models and spatial proximity. Clustering and classification plays a vital role in data mining. Clustering groups the similar data together based on the characteristics they possess. Clustering, which is so much used in pattern recognition, reduces the searching load. Leader-follower algorithm is the simplest one. K-means clustering algorithm clusters the similar data with the help of the mean value and squared error criterion whereas in k-medians algorithm, median value is used.

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