Towards Iteratively Analyzing the Distribution of Multi-Dimensional Data

摘要

Nowadays a lot of approaches have been designed for clustering and outlier detection methods. We observe that, in many situations, clusters and outliers are concepts whose meanings are inseparable to each other, especially for those data sets with noise. Extending the previous work in [15, 13] we design a cluster-outlier iterative detection algorithm, tending to detect the clusters and outliers in another perspective for noisy data sets. In this algorithm, clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. The adjustment and modification of the clusters and outliers are performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields such as pattern recognition, data clustering and signal processing. Experimental results demonstrate the advantages of our approach.