furnace operation profile.In addition,domestic scholars had given effective improvement measures for its shortcomings of sensitivity to the initial center and requirements for data distribution.TwoStep algorithm was an improved BRICH algorithm,which reduced the time complexity,and could automatically determine the optimal number of clusters.The authors of this article considered the problem that indicators for evaluating furnace operation profile were multiple and large overlapped.Principal Component Analysis was introduced on the basis of TwoStep algorithm,and three new core indicators were generated from the traditional evaluation indicators for the clustering results of furnace operation profile.It also showed good performance in the application of blast furnace operation profile monitoring and management.In this paper,K-Means and TwoStep were used to cluster the data set.Based on the principles of the two clustering algorithms,combined with Davies-Bouldin indicator and Dunn indicator,the clustering results were analyzed to judge the difference between the two clustering algorithms and shown a conclusion that the K-Means algorithm clustering results were better based on th sample data and data characteristics selected in this article.This research could provide a powerful selection 录用稿件，非最终出版 among different clustering algorithms in blast furnace ironmaking big data analysis KEY WORDS management of furnace operation profile;K-Means; Tw ies-Bouldin indicator;Dunn indicatorfurnace operation profile. In addition, domestic scholars had given effective improvement measures for its shortcomings of sensitivity to the initial center and requirements for data distribution. TwoStep algorithm was an improved BRICH algorithm, which reduced the time complexity, and could automatically determine the optimal number of clusters. The authors of this article considered the problem that indicators for evaluating furnace operation profile were multiple and large overlapped. Principal Component Analysis was introduced on the basis of TwoStep algorithm, and three new core indicators were generated from the traditional evaluation indicators for the clustering results of furnace operation profile. It also showed good performance in the application of blast furnace operation profile monitoring and management. In this paper, K-Means and TwoStep were used to cluster the data set. Based on the principles of the two clustering algorithms, combined with Davies-Bouldin indicator and Dunn indicator, the clustering results were analyzed to judge the difference between the two clustering algorithms and shown a conclusion that the K-Means algorithm clustering results were better based on the sample data and data characteristics selected in this article. This research could provide a powerful reference for the selection among different clustering algorithms in blast furnace ironmaking big data analysis. KEY WORDS management of furnace operation profile; K-Means; TwoStep; clustering; Davies-Bouldin indicator; Dunn indicator 录用稿件，非最终出版稿