  The conventional clustering algorithms mine static databases and generate a
set of patterns in the form of clusters. Many real life databases keep growing
incrementally. For such dynamic databases, the patterns extracted from the
original database become obsolete. Thus the conventional clustering algorithms
are not suitable for incremental databases due to lack of capability to modify
the clustering results in accordance with recent updates. In this paper, the
author proposes a new incremental clustering algorithm called CFICA(Cluster
Feature-Based Incremental Clustering Approach for numerical data) to handle
numerical data and suggests a new proximity metric called Inverse Proximity
Estimate (IPE) which considers the proximity of a data point to a cluster
representative as well as its proximity to a farthest point in its vicinity.
CFICA makes use of the proposed proximity metric to determine the membership of
a data point into a cluster.
