Using Table Based AHC Algorithm for Clustering Words in Domain on Current Affairs
Duke Taeho Jo · 2018
In this research, we propose the AHC version where words are encoded into tables, instead of numerical vectors, as the approach to the word clustering. In the previous works, the better text categorization performances from encoding texts into tables than into numerical vectors are shown and we need to reinforce both the word clustering and the text clustering by connecting them with each other. In this research, words are encoded into tables each of which consists of entries of text identifiers and their weights, the similarity metric between two tables which is based on the ratio of intersection to union, is defined, and the AHC algorithm is modified by adopting the proposed similarity metric. We adopt the clustering index as the evaluation metric, and validate empirically that the proposed AHC version is better than the traditional version. In future, we connect mutually the word clustering with the text clustering for reinforcing them at same time.