Visualisation and Clustering by 3D Cellular Automata: Application to Unstructured Data

View the data explosion and the very high volume circulating on the web (satellite data, genomic data,…) the classification of data (technique of data mining) becomes necessary. Unsupervised classification carried out by a bio inspired method (3D cellular automata). Experimentation in 2D was already carried out by Hamou& al in 2009, 2010 [1–3] and three dimension (3D) experiment was conducted to address the problem of the spatiality of the structure that we met in cellular automata 2D i.e. to do the non-supervised classification of large number of textual documents requires a very large 2D cellular automaton to represent the resulting clusters and exploit the power of visualisation of cellular automata 3D and its structure. Given the limited performance of 2D cellular automata in terms of space when the number of documents increases and in terms of visualisation clusters, our motivation was to experiment these cellular automata by increasing the size to view the impact of size on quality of results. The representation of textual data was carried out by a vector model whose components are derived from the overall balancing of the used corpus “Term Frequency–Inverse Document Frequency” (TF - IDF). The WorldNet thesaurus has been used to address the problem of the lemmatisation of the words because the representation used in this study is that of the bags of words. Another independent method of the language was used to represent textual records is that of the n-grams. Several measures of similarity have been tested. To validate the classification we have used two measures of assessment based on the recall and precision (f-measure and entropy). The results are promising and confirm the idea to increase the dimension to the problem of the spatiality of the classes. The results obtained in terms of purity class (i.e. the minimum value of entropy) shows that the number of documents over longer believes the results are better for 3D cellular automata, which was not obvious to the dimension 2. In terms of spatial navigation, cellular automata provide very good 3D performance visualisation than 2D cellular automata.