COID: A cluster–outlier iterative detection approach to multi-dimensional data analysis

Nowadays, most data mining algorithms focus on clustering methods alone. Also, there are a lot of approaches designed for outlier detection. 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. Thus, it is necessary to treat both clusters and outliers as concepts of the same importance in data analysis. In this paper, we present our continuous work on the cluster–outlier iterative detection algorithm (Shi in SubCOID: exploring cluster-outlier iterative detection approach to multi-dimensional data analysis in subspace. Auburn, pp. 132–135, 2008; Shi and Zhang in Towards exploring interactive relationship between clusters and outliers in multi-dimensional data analysis. IEEE Computer Society. Tokyo, pp. 518–519, 2005) 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.