Journal of Korea Game Society (한국게임학회 논문지)

Korea Game Society (한국게임학회)
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Extended Information Entropy via Correlation for Autonomous Attribute Reduction of BigData

빅 데이터의 자율 속성 감축을 위한 확장된 정보 엔트로피 기반 상관척도

  • Park, In-Kyu (Dept. of Game Software, College of Engineering Joongbu University)
  • 박인규 (중부대학교 게임 소프트웨어학과)
  • Received : 2017.12.18
  • Accepted : 2018.02.05
  • Published : 2018.02.20
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Abstract

Various data analysis methods used for customer type analysis are very important for game companies to understand their type and characteristics in an attempt to plan customized content for our customers and to provide more convenient services. In this paper, we propose a k-mode cluster analysis algorithm that uses information uncertainty by extending information entropy to reduce information loss. Therefore, the measurement of the similarity of attributes is considered in two aspects. One is to measure the uncertainty between each attribute on the center of each partition and the other is to measure the uncertainty about the probability distribution of the uncertainty of each property. In particular, the uncertainty in attributes is taken into account in the non-probabilistic and probabilistic scales because the entropy of the attribute is transformed into probabilistic information to measure the uncertainty. The accuracy of the algorithm is observable to the result of cluster analysis based on the optimal initial value through extensive performance analysis and various indexes.

고객 유형 분석에 쓰이는 다양한 데이터 분석 방법은 고객들을 위한 맞춤형 콘텐츠를 기획하고, 보다 편리한 서비스를 제공하기 위하여 고객들의 유형과 특성을 정확히 파악하는 것이 매우 중요하다. 본 논문에서는 정보의 손실을 줄이기 위한 일환으로 정보 엔트로피를 확장하여 속성의 불확실성을 이용한 k-modes 군집분석 알고리즘을 제안한다. 따라서 속성에 대한 유사도의 측정은 두 가지의 측면에서 고려되어진다. 하나는 각 분할의 중심에 대한 각 속성간의 불확실성을 측정하는 것이고, 다른 하나는 각 속성이 가지는 불확실성에 대한 확률적 분포에 대한 불확실성을 측정하는 것이다. 특히 속성내의 불확실성은 속성의 엔트로피를 확률적 정보로 변환하여 불확실성을 측정하기 때문에 최종적인 불확실성은 비확률적인 척도와 확률적인 척도에서 고려되어 진다. 여러 실험과 척도를 통하여 제안한 알고리즘의 정확도가 최적의 초기치를 기반으로 군집분석을 수행한 결과에 준수함을 보인다.

Keywords

References

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