CAJ | 학술논문

粒计算是研究和模拟人类认知从多粒度、多层次解决问题的方法，近年来成为智能信息处理中一个热点方向。云模型是一个基于概率理论研究定性定量转换认知模型的粒计算方法，通过正向和逆向云算法实现一组数据样本和一个基本概念之间的转换，但是目前的算法不能在整个问题域中解决多粒度、多概念的生成问题。概率统计中的高斯混合模型可以将任何一个频率分布函数转换成多个高斯分布的叠加，在此基础上，创新地提出用云模型中数字特征构建概念含混度作为概念外延共识程度的衡量，设计并实现了高斯云变换算法，将问题域中的数据分布自动转换为多粒度的不同概念，构建出人类概念认知中的泛概念树。通过在数据概念聚类和图像分割中的应用，验证了方法的有效性。
립계산시연구화모의인류인지종다립도、다층차해결문제적방법，근년래성위지능신식처리중일개열점방향。운모형시일개기우개솔이론연구정성정량전환인지모형적립계산방법，통과정향화역향운산법실현일조수거양본화일개기본개념지간적전환，단시목전적산법불능재정개문제역중해결다립도、다개념적생성문제。개솔통계중적고사혼합모형가이장임하일개빈솔분포함수전환성다개고사분포적첩가，재차기출상，창신지제출용운모형중수자특정구건개념함혼도작위개념외연공식정도적형량，설계병실현료고사운변환산법，장문제역중적수거분포자동전환위다립도적불동개념，구건출인류개념인지중적범개념수。통과재수거개념취류화도상분할중적응용，험증료방법적유효성。
Granular computing is a method for simulating human cognition to solve problems from different granularities and levels, and has become an important research direction in intelligent information processing. As one of granular com-puting methods, Cloud model is a qualitative and quantitative transformation cognition model based on probability theory. The present cloud model algorithms cannot resolve how to extract multiple concepts with different granularities from a data set on problem domain. Gaussian Mixture Model(GMM)is an important mathematical model, which can transfer an arbi-trary probability distribution to sum of some Gaussian distributions. Based on this, this paper proposes a measurement of the confusion degree of concepts based on the atomized feature, and designs the Gaussian Cloud Transformation algorithm (GCT). GCT transfers a data set on problem domain to some concepts on different granularities automatically, and pan concept tree is also formed in this process. Applications to data clustering and image concept extraction are presented to validate the effectiveness of the proposed method.