Abstract:
In the current digital era, data is generated enormously with fast growth from
different sources, and managing such huge data is a big challenge. Clustering
algorithm is able to find hidden patterns and extract useful information from
huge datasets. Among the clustering techniques, k-means clustering algorithm
is the most commonly used unsupervised classification technique to determine
the optimal number of clusters (k). However, the choice of the optimal number
of clusters (k) is a prominent problem in the process of the k-means clustering
algorithm. In most cases, clustering huge data, k is pre-determined by
researcher, and incorrectly chosen k leads to increase computational cost. In
order to obtain the optimal number of clusters, a distance-based k-mean
algorithm was proposed with a simulated dataset. In the k-means algorithm,
two distance measures were considered namely Euclidean and Manhattan
distances. The results based on simulated data reveal that the k-means
algorithm with Euclidean distance yields the optimal number of clusters
compared to Manhattan distance. Testing on real datasets shows consistent
results as the simulated ones.
