Document clustering method using dimension reduction and support vector clustering to overcome sparseness

Sunghae Jun, Sang Sung Park, Dong Sik Jang

Research output: Contribution to journalArticlepeer-review

99 Citations (Scopus)


Many studies on developing technologies have been published as articles, papers, or patents. We use and analyze these documents to find scientific and technological trends. In this paper, we consider document clustering as a method of document data analysis. In general, we have trouble analyzing documents directly because document data are not suitable for statistical and machine learning methods of analysis. Therefore, we have to transform document data into structured data for analytical purposes. For this process, we use text mining techniques. The structured data are very sparse, and hence, it is difficult to analyze them. This study proposes a new method to overcome the sparsity problem of document clustering. We build a combined clustering method using dimension reduction and K-means clustering based on support vector clustering and Silhouette measure. In particular, we attempt to overcome the sparseness in patent document clustering. To verify the efficacy of our work, we first conduct an experiment using news data from the machine learning repository of the University of California at Irvine. Second, using patent documents retrieved from the United States Patent and Trademark Office, we carry out patent clustering for technology forecasting.

Original languageEnglish
Pages (from-to)3204-3212
Number of pages9
JournalExpert Systems With Applications
Issue number7
Publication statusPublished - 2014 Jun 1

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (No. 2012-0026953). This work was supported by the Brain Korea 21 PLUS Project in 2013.


  • Dimension reduction
  • Document clustering
  • K-means clustering based on support vector clustering
  • Patent clustering
  • Silhouette measure
  • Sparseness problem

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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