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A Research on Visualization of Underlying Topics Based on MDS Model

Zhao Yiming;Zhang Jin;Li Yuanchu;Center for Studies of Information Resources,Wuhan University;School of Information Studies,University of Wisconsin-Milwaukee;Hubei Provincial Science & Technology Department;  
Database Tomography analysis applied term co-occurrence method to discover topics in full texts.But it may miss lots of content and topics in the original text set because of its procedure of co-occurrence frequency statistic and pre-selection of seed term.This paper propose to regard lexical cohesion as theoretical basis of underlying topics visualization,skipping the steps of co-occurrence frequency statistic and pre-selection of seed term,to present terms in transposed vector space,to map the proximity of terms in transposed vector space to visual space by Multi-Dimensional Scale(MDS) algorithm,and to discover and present topics by spatial clustering of related terms.Data coding method was introduced to overcome the limitations of MDS visual space area.Terms proximity matrix,centroid proximity matrix,attribute accumulative proximity matrix and according method procedures were developed to construct a three layers method system.Method of underlying topics visualization was successfully applied to do risk identification for public companies of computer application services,using verbal content about risk factor in prospectus as texts collection.
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