비분류표시 데이터의 초기예측을 통한 제약기반 부분-지도 군집분석

A Constraint-based Semi-supervised Clustering Through Initial Prediction of Unlabeled Data

Traditional clustering is regarded as an unsupervised teaming to analyze unlabeled data. Semi-supervised clustering uses a small amount of labeled data to predict labels of unlabeled data as well as to improve clustering performance. Previous methods use constraints generated from available labeled data in clustering process. We propose a new constraint-based semi-supervised clustering method by reflecting initial predicted labels of unlabeled data. We evaluate and compare the performance of the proposed method in terms of classification errors through numerical experiments with blinded labeled data.