Clustering based feature selection using Partitioning Around Medoids (PAM)

ISMI, DEWI PRAMUDI and Murinto, Murinto Clustering based feature selection using Partitioning Around Medoids (PAM). [Artikel Dosen]

Text Clustering Based Feature Selection using PAM.pdf Download (662kB)

Abstract

High-dimensional data contains a large number of features. With many features,
high dimensional data requires immense computational resources, including space
and time. Several studies indicate that not all features of high dimensional data are
relevant to classification result. Dimensionality reduction is inevitable and is
required due to classifier performance improvement. Several dimensionality
reduction techniques were carried out, including feature selection techniques and
feature extraction techniques. Sequential forward feature selection and backward
feature selection are feature selection using the greedy approach. The heuristics
approach is also applied in feature selection, using the Genetic Algorithm, PSO, and
Forest Optimization Algorithm. PCA is the most well-known feature extraction
method. Besides, other methods such as multidimensional scaling and linear
discriminant analysis. In this work, a different approach is applied to perform feature
selection. Cluster analysis based feature selection using Partitioning Around
Medoids (PAM) clustering is carried out. Our experiment results showed that
classification accuracy gained when using feature vectors' medoids to represent the
original dataset is high, above 80%.

Item Type:	Artikel Dosen
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Depositing User:	Dewi Pramudi Ismi
Date Deposited:	06 Nov 2020 03:11
Last Modified:	06 Nov 2020 03:11
URI:	http://eprints.uad.ac.id/id/eprint/21087

Actions (login required)

View Item