CAJ | 학술논문

半监督聚类是机器学习的重要研究内容之一，它通过利用样本层面的少量标记数据信息或者利用特征层面的特征偏好信息来指导半监督聚类。但现有的半监督聚类算法仅考虑了单一层面的半监督先验信息，罕有同时考虑两个不同层面的此类信息进行半监督聚类。为了弥补这一遗漏，联合利用特征层面给定的特征偏好，即特征之间的相对重要性关系，并结合样本层面的少量标记数据等半监督信息，在传统的半监督聚类算法基础上发展出一个扩展型半监督聚类算法。初步实验验证了该算法的有效性。
반감독취류시궤기학습적중요연구내용지일，타통과이용양본층면적소량표기수거신식혹자이용특정층면적특정편호신식래지도반감독취류。단현유적반감독취류산법부고필료단일층면적반감독선험신식，한유동시고필량개불동층면적차류신식진행반감독취류。위료미보저일유루，연합이용특정층면급정적특정편호，즉특정지간적상대중요성관계，병결합양본층면적소량표기수거등반감독신식，재전통적반감독취류산법기출상발전출일개확전형반감독취류산법。초보실험험증료해산법적유효성。
Semi-supervised clustering is one of the important research subjects in the machine learning community. It guides semi-supervised clustering by using the label information of a small amount of data or the information of relative preference relations between features. However, the only single-facet information is considered as prior knowledge in existing semi-supervised clustering algorithms. It is relatively rare to jointly use information from two different facets in pattern and feature into semi-supervised clustering. To remedy such shortcoming, based on tradi-tional semi-supervised clustering algorithms, this paper proposes an extended semi-supervised clustering algorithm by jointly exploiting both given feature preferences in feature facet and semi-supervised information of a small amount of data in pattern facet. The experimental results show its effectiveness.