Generalized K-means Algorithm On
Nominal Dataset
Author(s)
S. H. Al-Harbi & A. M. Al-Shahri
Abstract
Clustering has typically been a problem related to continuous fields. However, in
data mining, often the data values are nominal and cannot be assigned meaningful
continuous substitutes. The largest advantage of the k-means algorithm in data
mining applications is its efficiency in clustering large data sets. The k-means algorithm
usually uses the simple Euclidean metric which is only suitable for hyperspherical
clusters, and its use is limited to numeric data. This paper extends our
work on the DCV metric which was introduced to deal with nominal data, and
then demonstrates how the popular k-means clustering algorithm can be profitably
modified to deal with the DCV metric. Having adapted the k-means algorithm, the
DCV metric will be implemented and the results examined.With this development,
it is now possible to improve the results of cluster analyses on nominal data sets.
Keywords: clustering, data mining, Mahalanobis metric, DCV metric, Hamming
metric, k-means.
1 Introduction
A way of extracting information from a large data set is to cluster it. Clustering
involves assigning objects into groups such that the objects in a group are similar
to each other, but different from the objects in the other groups. Similarity is
fundamental to the definition of a cluster and being able to measure the similarity
of two objects in the same feature space is essential to most clustering algorithms.
In a metric space, the dissimilarity between two objects is modelled with a distance
function that satisfies the triangle inequality. It gives a numerical value to the
notion of closeness between two objects in a high-dimensional space. More details
of metric spaces can be found, for example, in [3]. Applications of clustering exist
in diverse areas, e.g.
Keywords
clustering, data mining, Mahalanobis metric, DCV metric, Hamming
metric, k-means.
Related Book
Other papers in this volume
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