A similarity measure for text processing
Jung-Yi Jiang, Wenhao Cheng, Yu-Shu Chiou, Shie-Jue Lee · 2011
In this paper, we propose a novel similarity measure for document data processing. For two document vectors, the proposed measure takes three cases into account: a) The feature considered appears in both documents, b) the feature considered appears in only one document, and c) the feature considered appears in none of the documents. For the first case, we give a lower bound and decrease the similarity according to the difference between the feature values of the two documents. For the second case, we give a fixed value disregarding the magnitude of the feature value. For the last case, we treat it as an identity, Experimental results show that our proposed method can work more effectively than others.