Outlier Detection Based On Clustering Over Sensed Data Using HADOOP

Outliers are regarded as noisy data in statistics, has turned out to be an important problem which is being researched in diverse fields of research and application domains. Many outlier detection techniques have been developed specific to certain application domains, while some techniques are more generic. Some application domains are being researched in strict confidentiality such as research on crime and terrorist activities. The techniques and results of such techniques are not readily forthcoming.Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Cloud computing provides powerful and economical infrastructural resources for cloud users to handle ever-increasing Big Data with data-processing frameworks such as MapReduce.This work consider two clustering algorithms known as DBScan and K-Means and implemented with Intel Corporation's Sensed dataset. von Mourya, Morison