Sigit Adinugroho, Yuita Arum Sari, M. Ali Fauzi, Putra Pandu Adikara
Document clustering is an important tool to help managing the vast amount of digital text document. This paper introduces a new approach to cluster text document. First, text is preprocessed and indexed using inverted index. Then the index is trimmed using TF-DF thresholding. After that, Term Document Matrix is built based on TF-IDF. Next step uses Latent Semantic Indexing to extract important feature from Term Document Matrix. The following process is selecting seeds via Pillar algorithm. Based on determined seeds, K-Means clustering is performed. Experiment result proves that this approach outperforms standard K-Means document clustering. © 2017 IEEE.
Computer Vision Research Group, Faculty of Computer Science, Brawijaya University, Malang, Indonesia
