【刊名】Shuju Caiji Yu Chuli/Journal of Data Acquisition and Processing
【作者单位】aDepartment of Computer Science and Technology, Taiyuan Normal University, Taiyuan, 030619, China
【年份】2017
【卷号】Vol.32 No.6
【页码】1216-1222
【ISSN】1004-9037
【关键词】High dimensional data K-Means clustering Non-negative matrix factorization Sparse constraint
【摘要】 To improve the quality of K-Means clustering in high-dimensional data, a K-Means clustering algorithm is presented based on non-negative matrix factorization with sparseness constraints. The algorithm finds the low dimensional data structure embedded...
