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Performance Analysis of MCOD Algorithm With Varying Parameters
Abstract
Outlier detection has become one of the prominent and most needed technologies these days. Outliers can be anything in our daily life like credit card fraud, intrusion in a network, aberrant condition detection in condition monitoring data. There are numerous methodologies to detect outliers. In the past few years many tools have come up in the outlier detection in data streams. In this chapter, the authors discuss the tool MOA (massive online analysis) to detect anomalies and the best performing algorithm amongst the prescribed algorithms of MOA. The authors elaborately discuss that MCOD (micro-cluster-based algorithm) is one of the best in the prescribed algorithms of the MOA (massive online analysis) tool which outperforms all other algorithms. In this paper, the authors will deeply discuss the performance of MCOD algorithm. The authors will also discuss which factor of MCOD separates its performance from others and also what the different parameters that influence the performance of MCOD are.
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