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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Fine-Grained Mobile Application Clustering Model Using Retrofitted Document Embedding

  • Yoon, Yeo-Chan (SW & Content Research Laboratory, ETRI) ;
  • Lee, Junwoo (SW & Content Research Laboratory, ETRI) ;
  • Park, So-Young (Department of Game Design and Development, Sangmyung University) ;
  • Lee, Changki (Department of Computer Science, Kangwon National University)
  • Received : 2016.12.21
  • Accepted : 2017.05.08
  • Published : 2017.08.01
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Abstract

In this paper, we propose a fine-grained mobile application clustering model using retrofitted document embedding. To automatically determine the clusters and their numbers with no predefined categories, the proposed model initializes the clusters based on title keywords and then merges similar clusters. For improved clustering performance, the proposed model distinguishes between an accurate clustering step with titles and an expansive clustering step with descriptions. During the accurate clustering step, an automatically tagged set is constructed as a result. This set is utilized to learn a high-performance document vector. During the expansive clustering step, more applications are then classified using this document vector. Experimental results showed that the purity of the proposed model increased by 0.19, and the entropy decreased by 1.18, compared with the K-means algorithm. In addition, the mean average precision improved by more than 0.09 in a comparison with a support vector machine classifier.

Keywords

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