Clustering Balinese Script Image in Palm Leaf Using Hierarchical K-Means Algorithm

DOI

10.2991/aer.k.211129.009How to use a DOI?

Keywords

clustering and hierarchical clustering approach; Balinese Lontar script; hierarchical K-Means algorithm

Abstract

This paper proposes a combination approach of clustering, a hierarchical clustering to group similar characters of Balinese Lontar script; followed by k-means clustering as a way to identify the group to find-out the right label for members of the group. Based on the optimal value of the silhouette coefficient, the hierarchical clustering method results in 113 groups of Balinese Lontar scripts. Using these 113 groups to compute the initial centroid of each cluster, k-means clustering process is able to correctly group and label 111 scripts out of 198 Balinese Lontar scripts sample.

Copyright

© 2021 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

Download article (PDF)

TY  - CONF
AU  - Anastasia Rita Widiarti
AU  - C. Kuntoro Adi
PY  - 2021
DA  - 2021/11/30
TI  - Clustering Balinese Script Image in Palm Leaf Using Hierarchical K-Means Algorithm
BT  - Proceedings of the International Conference on Innovation in Science and Technology (ICIST 2020)
PB  - Atlantis Press
SP  - 38
EP  - 42
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.211129.009
DO  - 10.2991/aer.k.211129.009
ID  - Widiarti2021
ER  -