New Search Strategies And A New Derived
Inequality For Efficient K-medoids-based
Algorithms
Author(s)
C.-S. Chiang, S.-C. Chu, J. F. Chang & J.-S. Pan
Abstract
In this paper, a new inequality is derived which can be used for the problem of
nearest neighbor searching. We also present a searching technique referred to as
a previous medoid index to reduce the computation time particularly for the kmedoids-
based algorithms. A novel method is also proposed to reduce the computational
complexity by the utilization of memory. Four new search strategies
for k-medoids-based algorithms based on the new inequality, previous medoid
index, the utilization of memory, triangular inequality criteria and partial distance
search are proposed. Experimental results demonstrate that the proposed algorithm
applied to the CLARANS algorithm may reduce the computation time from 88.8%
to 95.3% with the same average distance per object comparing with CLALRANS.
The derived new inequality and proposed search strategies can also be applied to
the nearest neighbor searching and the other clustering algorithms.
1 Introduction
The goal of clustering , in general, is to group sets of objects into classes such that
homogeneous objects are placed in the same cluster while dissimilar objects are
in separate clusters. Clustering (or classification) techniques are common forms
Keywords
Related Book
Other papers in this volume
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