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Cluster Based Outlier Detection Algorithm for Healthcare Data
2015
Procedia Computer Science
Outliers has been studied in a variety of domains including Big Data, High dimensional data, Uncertain data, Time Series data, Biological data, etc. In majority of the sample datasets available in the repository, atleast 10% of the data may be erroneous, missing or not available. In this paper, we utilize the concept of data preprocessing for outlier reduction. We propose two algorithms namely Distance-Based outlier detection and Cluster-Based outlier algorithm for detecting and removing
doi:10.1016/j.procs.2015.04.058
fatcat:fokovmxv7vfhfpfqmghqnxvp7y