Self-Organizing Map vs Initial Centroid Selection Optimization to Enhance K-Means with Genetic Algorithm to Cluster Transcribed Broadcast News Documents

Abstract

A compilation of artificial intelligence techniques are employed in this research to enhance the process of clustering transcribed text documents obtained from audio sources. Many clustering techniques suffer from drawbacks that may cause the algorithm to tend to sub optimal solutions, handling these drawbacks is essential to get better clustering results and avoid sub optimal solutions. The main target of our research is to enhance automatic topic clustering of transcribed speech documents, and examine the difference between implementing the K-means algorithm using our Initial Centroid Selection Optimization (ICSO) [16] with genetic algorithm optimization with Chi-square similarity measure to cluster a data set then use a self-organizing map to enhance the clustering process of the same data set, both techniques will be compared in terms of accuracy. The evaluation showed that using K-means with ICSO and genetic algorithm achieved the highest average accuracy.

K-Means Clustering Dalam Pengelompokan Relevansi Pekerjaan S1 Informatika (Studi Kasus Jurusan Teknik Informatka Umm Malang)

“…Algoritma K-means telah banyak dibahas dalam literatur, dan memiliki beberapa kelemahan. Salah satu kelemahan utama adalah sensitivitasnya terhadap pemilihan awal pusat cluster, yang dapat berdampak signifikan terhadap hasil akhir pengelompokan [3]. Ketergantungan pada nilai awal sering kali menyebabkan kualitas hasil pengelompokan yang buruk, terutama ketika centroid awal tidak dipilih dengan tepat.…”