โ Scribed by Kopcsa, Alexander ;Schiebel, Edgar
No coin nor oath required. For personal study only.
Much effort has been done to develop more objective simple countings of bibliographic elements such as auquantitative methods to analyze and integrate survey inthors, articles, keywords, patents, etc., and their graphical formation for understanding research trends and rerepresentations. Two-dimensional relations are described search structures. Co-word analysis is one class of techby the so-called co-occurrences of keywords and citations niques that exploits the use of co-occurrences of items in publications, articles, patents, etc. in written information. However, there are some bottlenecks in using statistical methods to produce mappings The special problem, dealing with interrelational data, of reduced information in a comfortable manner. On one is their clear representation. Co-occurrences may be rephand, often used statistical software for PCs has restricresented easily by the use of matrices, but this type of tions for the amount for calculable data; on the other representation is only useful, if some 20 or 30 words are hand, the results of the multidimensional scaling routines are not quite satisfying. Therefore, this article intro-processed. In the case of hundreds of keywords, visual duces a new iteration model for the calculation of corepresentations are much more useful. Therefore, some word maps that eases the problem. The iteration model authors suggested different ways for the visual representais for positioning words in the two-dimensional plane tion of the whole co-occurrence matrix, like circular or due to their connections to each other, and it consists of vertical maps (Rip & Courtial, 1984), and co-word maps a quick and stabile algorithm that has been implemented with software for personal computers. A graphic module or co-classification maps (Engelsman & Van Raan, 1991) represents the data in well-known ''technology maps.''
for two-dimensional representation. Leydesdorff (1989) proposed the cluster analysis as a one-dimensional projection of the co-occurrence matrix.
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