Himanshu Dahiya
Vol. 5, Issue 1, Jan-Jun 2018
Abstract:
An important application of outlier detection like normal and abnormal action detection, animal behaviour alter, etc. It’s a hard issue since global data about information regarding data divisions must be called to verify the outliers. In this paper, I discussed the proposed approach in the research area. In the proposed work, I divide the data into two clusters, i.e., Cluster1 and Cluster2. We implement K-means clustering to divide the data into two sections, detected or not detected data. We optimize the outlier data with the bacteria Foraging Optimization approach. In BFOA, algorithm based on further steps: (i) population Size (ii) Rotation (tumble and swim) (iii) dispersal (iv) reproduction of the abnormal data. This means BFOA optimizes the relevant data. The classification algorithm is used to classify the outliers based on the training and testing phase. In this technique, to use and optimize the communication cost. Later, grouped data in a single position for centralized processing.
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