한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제20권4호
  • /
  • Pages.17-23
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  • 2015
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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모바일 앱 트렌드를 고려한 2단계 군집화 방법

Two-Phase Clustering Method Considering Mobile App Trends

  • Heo, Jeong-Man (Dept. of Game Design & Development, SangMyung University) ;
  • Park, So-Young (Dept. of Game Design & Development, SangMyung University)
  • 투고 : 2014.11.13
  • 심사 : 2015.03.16
  • 발행 : 2015.04.30
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초록

본 논문에서는 단어 군집을 사용하여 모바일 앱을 군집화하는 방법을 제안한다. 모바일 앱 트렌드의 빠른 변화를 고려하여, 제안하는 방법은 미리 정의된 분류체계를 사용하지 않고, 모바일 앱 집합에 군집화 기술을 적용하여 의미적으로 유사한 모바일 앱을 묶는다. 짧은 모바일 앱 소개 글의 자료 부족 문제를 완화하기 위해서, 각 단어에 대해 unigram 뿐만 아니라, bigram, trigram, 단어 군집 정보를 추가적으로 확보하여 활용한다. 모바일 앱을 전체적으로 정확하게 군집화하기 위해서, 제안하는 방법은 단어 군집을 활용하여 모바일 앱 군집의 크기가 지나치게 작거나 크지 않도록 관리한다. 실험결과 제안하는 방법은 단어 군집을 활용하여 전체 정확도를 57.48%에서 79.66%로 22.18% 개선시켰다.

In this paper, we propose a mobile app clustering method using word clusters. Considering the quick change of mobile app trends, the proposed method divides the mobile apps into some semantically similar mobile apps by applying a clustering algorithm to the mobile app set, rather than the predefined category system. In order to alleviate the data sparseness problem in the short mobile app description texts, the proposed method additionally utilizes the unigram, the bigram, the trigram, the cluster of each word. For the purpose of accurately clustering mobile apps, the proposed method manages to avoid exceedingly small or large mobile app clusters by using the word clusters. Experimental results show that the proposed method improves 22.18% from 57.48% to 79.66% on overall accuracy by using the word clusters.

키워드

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