Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
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A Study on Optimizing the Number of Clusters using External Cluster Relationship Criterion

외부 군집 연관 기준 정보를 이용한 군집수 최적화

  • 이현진 (한국사이버대학교 컴퓨터정보통신학과) ;
  • 지태창 (연세대학교 컴퓨터과학과)
  • Received : 2011.09.02
  • Accepted : 2011.09.16
  • Published : 2011.09.30
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Abstract

The k-means has been one of the popular, simple and faster clustering algorithms, but the right value of k is unknown. The value of k (the number of clusters) is a very important element because the result of clustering is different depending on it. In this paper, we present a novel algorithm based on an external cluster relationship criterion which is an evaluation metric of clustering result to determine the number of clusters dynamically. Experimental results show that our algorithm is superior to other methods in terms of the accuracy of the number of clusters.

군집화는 주어진 데이터를 분할하여 데이터 속에 숨겨져 있는 의미를 자동으로 발견하는 방법이다. k-means는 간단하고 빠른 군집화 알고리즘 중의 하나이다. 군집의 수 k는 군집화를 수행하는데 매우 중요한 요소이며, k의 값에 의해 군집화 결과가 달라진다. 본 논문에서는 반복적인 k-means 수행과 군집의 품질을 평가하는 외부 군집 연관 기준 정보를 결합하여 최적의 군집수를 결정하는 방법을 제안한다. 실험 결과 기존의 방법들에 비하여 제안하는 방법이 군집수의 정확성 측면에서 우수한 성능을 보였다.

Keywords

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