A Review on an Outlier Detection Using Clustering Algorithms and Optimization Techniques

Abstract

Clustering is the task of assigning a set of data objects into groups called clusters so that the objects in the same cluster are more similar in some sense to each other than to those in other cluster. Data items whose values are different from rest of the data or whose values fall outside the described range are called outliers. Outlier detection is an important issue in data mining, where it is used to identify and eliminate anomalous data objects from given data set. Outlier detection is an essential step in the data mining process. Its main purpose to remove the incompatible data from the original data. This purpose helps in the removal of data which necessary for carrying out to speed up the applications like classification, data perturbation and compression. It plays an important role in the weather forecasting, performance analysis of sports person and network intrusion detection systems. The outlier for the single variable can be easily observed but for the n-variable it becomes a tedious process. To enhance the performance of outlier detection in n-variable or attributes several methods were discussed. This paper provides a brief survey on clustering techniques and outlier detection techniques. Particularly the k-means outlier detection and Optimization Techniques for outlier detection is discussed.Clustering is the task of assigning a set of data objects into groups called clusters so that the objects in the same cluster are more similar in some sense to each other than to those in other cluster. Data items whose values are different from rest of the data or whose values fall outside the described range are called outliers. Outlier detection is an important issue in data mining, where it is used to identify and eliminate anomalous data objects from given data set. Outlier detection is an essential step in the data mining process. Its main purpose to remove the incompatible data from the original data. This purpose helps in the removal of data which necessary for carrying out to speed up the applications like classification, data perturbation and compression. It plays an important role in the weather forecasting, performance analysis of sports person and network intrusion detection systems. The outlier for the single variable can be easily observed but for the n-variable it becomes a tedious process. To enhance the performance of outlier detection in n-variable or attributes several methods were discussed. This paper provides a brief survey on clustering techniques and outlier detection techniques. Particularly the k-means outlier detection and Optimization Techniques for outlier detection is discussed.