Hybrid Approach for Outlier Detection in High Dimensional Dataset

An object that does not obey the behavior of normal data objects is called as Outlier. In many data analysis process, a large number of data are being recorded or sampled as data set. It is very important in data mining to find rare events, anomalies, exceptions etc. Outlier detection has important applications in many fields in which the data can contain high dimensions. Resulting the intended knowledge of outliers will become inefficient and even infeasible in high dimensional space. I devised an outlier detection structure which is based on clustering. Clustering is an unsupervised type of data mining and it does not require trained or labeled data. Combination of density based and partition clustering method for taking improvement of both densities based and distance based outlier detection. Weights are allocated to attributes depending upon their individual significance in mining task and weights are adaptive in nature. Weighted attributes are useful to reduce or remove the effect of noisy attributes. In view of the challenges of streaming data, the schemes are incremental and adaptive to concept development. In high dimensional data the number of attributes associated with the dataset is very large and it makes the dataset unmanageable. Thus a Feature Extraction technique is used to reduce the number of attributes to a manageable value.