Affiliations: Department of Information Engineering and Computer
Science, Feng Chia University, 100 Wen Hwa Road, Taichung, Taiwan.
E-mail: [email protected]; [email protected]
Abstract: Clustering is useful for mining the underlying structure of a
dataset in order to support decision making since target or high-risk groups
can be identified. However, for high dimensional datasets, the result of
traditional clustering methods can be meaningless as clusters may only be
depicted with respect to a small part of features. Taking customer datasets as
an example, certain customers may correlate with their salary and education,
and the others may correlate with their job and house location. If one uses all
the features of a customer for clustering, these local-correlated clusters may
not be revealed. In addition, processing high dimensions and large datasets is
a challenging problem in decision making. Searching all the combinations of
every feature with every record to extract local-correlated clusters is
infeasible, which is in exponential scale in terms of data dimensionality and
cardinality. In this paper, we propose a scalable 2-Leveled Approximated
Hyper-Image-based Clustering framework, referred as 2L-HIC-A, for mining
local-correlated clusters, where each level clustering process requires only
one scan of the original dataset. Moreover, the data-processing time of
2L-HIC-A can be independent of the input data size. In 2L-HIC-A, various
well-developed image processing techniques can be exploited for mining
clusters. In stead of proposing a new clustering algorithm, our framework can
accommodate other clustering methods for mining local-corrected clusters, and
to shed new light on the existing clustering techniques.
Keywords: local-correlated cluster, approximated clustering, high dimension, large dataset, image processing, morphology
