Abstract
Anomaly detection in heterogeneous severity feature space is a challenging issue for which only a few models have been designed and developed. Detection of spatial objects and its patterns helps to find essential spatial decision patterns from large spatial datasets. Traditional spatial anomaly detection techniques are failed to process and detect anomalies due to noise, sparsity and imbalance problems. Also, most of the traditional statistical anomaly detection models consider the homogeneous type of objects for outlier detection and ignore the effect of heterogeneous objects. In this proposed model, a novel statistical pre-processing based spatial anomaly detection model was proposed to find the anomalies on cyclone dataset. Experimental results proved that the proposed model has high computational detection rate with less mean error rate compared to the traditional anomaly detection models.
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Malyala, L.P., Rao, N.S. (2018). A Novel Statistical Pre-processing Based Spatial Anomaly Detection Model on Cyclone Dataset. In: Bhattacharyya, P., Sastry, H., Marriboyina, V., Sharma, R. (eds) Smart and Innovative Trends in Next Generation Computing Technologies. NGCT 2017. Communications in Computer and Information Science, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-10-8657-1_46
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DOI: https://doi.org/10.1007/978-981-10-8657-1_46
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