Concept association and hierarchical Hamming clustering model in text classification

• Published in: Wuhan University journal of natural sciences Vol. 9; no. 3; pp. 339 - 342
• Main Authors: Su, Gui-yang, Li, Jian-hua, Ma, Ying-hua, Li, Shenghong, Yin, Zhong-hang
• Format: Journal Article
• Language: English; Chinese
• Published: Heidelberg Springer Nature B.V 01.05.2004; School of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200030, China
• Subjects: Classification; Clustering; Documents; Vector spaces; hamming clustering; concept association; hierarchical clustering; text classification
• ISSN: 1007-1202; 1993-4998
• DOI: 10.1007/BF02907890

We propose two models in this paper. The concept of association model is put forward to obtain the co-occurrence relationships among keywords in the documents and the hierarchical Hamming clustering model is used to reduce the dimensionality of the category feature vector space which can solve the problem of the extremely high dimensionality of the documents' feature space. The results of experiment indicate that it can obtain the co-occurrence relations among key-words in the documents which promote the recall of classification system effectively. The hierarchical Hamming clustering model can reduce the dimensionality of the category feature vector efficiently, the size of the vector space is only about 10% of the primary dimensionality.