Data Clustering via Dimension Reduction and Algorithm Aggregation

dimension reduction;nonnegative matrix factorization;document clustering;data clustering;singular value decomposition;clustering algorithms

Race, Shaina L ; Ernest Stitzinger, Committee Member,Carl Meyer, Committee Chair,Ilse Ipsen, Committee Member,Race, Shaina L ; Ernest Stitzinger ; Committee Member ; Carl Meyer ; Committee Chair ; Ilse Ipsen ; Committee Member

University:North Carolina State University

关键词: dimension reduction;    nonnegative matrix factorization;    document clustering;    data clustering;    singular value decomposition;    clustering algorithms;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/2029/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

We focus on the problem of clustering large textual data sets. We present 3 well-known clustering algorithms and suggest enhancements involving dimension reduction. We propose a novel method of algorithm aggregation that allows us to use many clustering algorithms at once to arrive on a single solution. This method helps stave off the inconsistency inherent in most clustering algorithms as they are applied to various data sets. We implement our algorithms on several large benchmark data sets.