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

  • Authors:

  • Sunghae Jun;Sang-Sung Park;Dong-Sik Jang

  • Affiliations:

  • Department of Statistics, Cheongju University, 298, Daeseong-ro Sangdang-gu, Cheongju, Chungbuk 360-764, Republic of Korea;Graduate School of Management of Technology, Korea University, 1, 5-Ka, Anam-dong Sungbuk-ku, Seoul 136-701, Republic of Korea;Division of Industrial Management Engineering, Korea University, 1, 5-Ka, Anam-dong Sungbuk-ku, Seoul 136-701, Republic of Korea

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2014

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Abstract

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.