Text data sets can be represented using models that do not preserve text structure, or using models that preserve text structure. Our hypothesis is that depending on the data set nature, there can be advantages using a model that preserves text structure over one that does not, and vice versa. The key is to determine the best way of representing a particular data set, based on the data set itself. In this work, we proposde B“orjae to investigate this problem by combining text distortion and algorithmic clustering based on string compression. Specifically, a distortion technique previously developed by the authors is applied to destroy text structure progressively. Following this, a clustering algorithm based on string compression is used to analyze the effects of the distortion on the information contained in the texts. Several experiments are carried out on text data sets and artificially-generated data sets. The results show that in strongly structural data sets the clustering results worsen as text structure is progressively destroyed. Besides, they show that using a compressor which enables the choice of the size of the left-context symbols helps to determine the nature of the data sets. Finally, the results are contrasted with a method based on multidimensional projections and analogous conclusions are obtained.

A wide range of networks, including those with small-world topology, can be modeled by the connectivity ratio and randomness of the links. Both learning and attractor abilities of a neural network can be measured by the mutual information (MI) as a function of the load and the overlap between patterns and retrieval states. In this letter, we use MI to search for the optimal topology with regard to the storage and attractor properties of the network in an Amari-Hopfield model. We find that while an optimal storage implies an extremely diluted topology, a large basin of attraction leads to moderate levels of connectivity. This optimal topology is related to the clustering and path length of the network. We also build a diagram for the dynamical phases with random or local initial overlap and show that very diluted networks lose their attractor ability.