A Novel Density-Based Clustering Approach for Outlier Detection in High-Dimensional Data

Outlier detection is a primary aspect in data-mining and machine learning applications, also known as outlier mining. The importance of outlier detection in medical data came from the fact that outliers may carry some precious information however outlier detection can show very bad performance in the presence of high dimensional data. In this paper, a new outlier detection technique is proposed based on a feature selection strategy to avoid the curse of dimensionality, named Infinite Feature Selection DBSCAN. The main purpose of our proposed method is to reduce the dimensions of a high dimensional data set in order to efficiently identify outliers using clustering techniques. Simulations on real databases proved the effectiveness of our method taking into account the accuracy, the error-rate, F-score and the retrieval time of the algorithm.