비지도학습 데이터의 정확성 측정을 위한 클러스터별 분류 평가 예측 모델에 대한 연구

• Published in: 멀티미디어학회논문지 Vol. 21; no. 7; pp. 779 - 786
• Main Authors: 정세훈(Se Hoon Jung), 김종찬(Jong Chan Kim), 김치용(Kim Cheeyong), 유강수(Kang Soo You), 심춘보(Chun Bo Sim)
• Format: Journal Article
• Language: Korean
• Published: 한국멀티미디어학회 2018
• Subjects: RNN; LSTM; Unsupervised Learning; Clustering k-LSTM RNN; Cluster Verification; Classification
• ISSN: 1229-7771; 2384-0102

In this paper, we are applied a nerve network to allow for the reflection of data learning methods in their overall forms by using cluster data rather than data learning by the stages and then selected a nerve network model and analyzed its variables through learning by the cluster. The CkLR algorithm was proposed to analyze the reaction variables of clustering outcomes through an approach to the initialization of K-means clustering and build a model to assess the prediction rate of clustering and the accuracy rate of prediction in case of new data inputs. The performance evaluation results show that the accuracy rate of test data by the class was over 92%, which was the mean accuracy rate of the entire test data, thus confirming the advantages of a specialized structure found in the proposed learning nerve network by the class.