Can I read SubCOID: an attempt to explore cluster-outlier iterative detection approach to multi-dimensional data analysis in subspace on EtoBox?

SubCOID: an attempt to explore cluster-outlier iterative detection approach to multi-dimensional data analysis in subspace by Yong Shi is a scholarly article available to read on EtoBox.

What is SubCOID: an attempt to explore cluster-outlier iterative detection approach to multi-dimensional data analysis in subspace about?

## Many data mining algorithms focus on clustering methods. There are also 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. Clusters and outliers should be treated as the concepts of the same importance in data analysis. In our previous work [22] we proposed a cluster-outlier iterative detection algorithm in full data space. However, in high dimensional spaces, for a given cluster or outlier, not all dimensions may be relevant to it. In this paper we extend our work in subspace area, tending to detect the clusters and outliers in another perspective for noisy data. Each cluster is associated with its own subset of dimensions, so is each outlier. The partition, subsets of dimensions and qualities of clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. This process is performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields such as pattern re

Author

Yong Shi

Publisher

ACM

Published

2008

Language

EN