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Given the limited performance of 2D cellular automata in terms of space whenthe number of documents increases and in terms of visualization clusters, ourmotivation was to experiment these cellular automata by increasing the size toview the impact of size on quality of results. The representation of textualdata was carried out by a vector model whose components are derived from theoverall balancing of the used corpus, Term Frequency Inverse Document Frequency(TF-IDF). The WorldNet thesaurus has been used to address the problem of thelemmatization of the words because the representation used in this study isthat of the bags of words. Another independent method of the language was usedto represent textual records is that of the n-grams. Several measures ofsimilarity have been tested. To validate the classification we have used twomeasures of assessment based on the recall and precision (f-measure andentropy). The results are promising and confirm the idea to increase thedimension to the problem of the spatiality of the classes. The results obtainedin terms of purity class (i.e. the minimum value of entropy) shows that thenumber of documents over longer believes the results are better for 3D cellularautomata, which was not obvious to the 2D dimension. In terms of spatialnavigation, cellular automata provide very good 3D performance visualizationthan 2D cellular automata.

Visualization and clustering by 3D cellular automata: Application to unstructured data