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

TL;DR

This paper explores the use of 3D cellular automata for visualizing and clustering textual data, demonstrating improved spatial and clustering performance over 2D automata, especially with larger datasets.

Contribution

It introduces the application of 3D cellular automata to text clustering and visualization, showing enhanced results compared to traditional 2D automata.

Findings

3D automata outperform 2D in clustering quality with larger datasets.

3D visualization provides better spatial navigation and understanding.

Increasing automata dimension improves the spatiality of class representations.

Abstract

Given the limited performance of 2D cellular automata in terms of space when the number of documents increases and in terms of visualization 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 lemmatization 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 2D dimension. In terms of spatial navigation, cellular automata provide very good 3D performance visualization than 2D cellular automata.