BackgroundDNA Clustering is an important technology to automatically find the inherent relationships on a large scale of DNA sequences. But the DNA clustering quality can still be improved greatly. The DNA sequences similarity metric is one of the key points of clustering. The alignment-free methodology is a very popular way to calculate DNA sequence similarity. It normally converts a sequence into a feature space based on words’ probability distribution rather than directly matches strings. Existing alignment-free models, e.g. k-tuple, merely employ word frequency information and ignore many types of useful information contained in the DNA sequence, such as classifications of nucleotide bases, position and the like. It is believed that the better data mining results can be achieved with compounded information. Therefore, we present a new alignment-free model that employs compounded information to improve the DNA clustering quality.ResultsThis paper proposes a Category-Position-Frequency (CPF) model, which utilizes the word frequency, position and classification information of nucleotide bases from DNA sequences. The CPF model converts a DNA sequence into three sequences according to the categories of nucleotide bases, and then yields a 12-dimension feature vector. The feature values are computed by an entropy based model that takes both local word frequency and position information into account. We conduct DNA clustering experiments on several datasets and compare with some mainstream alignment-free models for evaluation, including k-tuple, DMk, TSM, AMI and CV. The experiments show that CPF model is superior to other models in terms of the clustering results and optimal settings.ConclusionsThe following conclusions can be drawn from the experiments. (1) The hybrid information model is better than the model based on word frequency only. (2) For DNA sequences no more than 5000 characters, the preferred size of sliding windows for CPF is two which provides a great advantage to promote system performance. (3) The CPF model is able to obtain an efficient stable performance and broad generalization.

It is easy to gather a huge amount of information from a battlefield by satellites, manned or unmanned aerial vehicles. But it becomes an important issue to effectively deal with the tremendous information and create reasonable solutions based on that. This paper introduces an Intelligent Force Deployment system DX-IFD, which combines topography heuristic information and military principles with the Genetic Algorithm to automatically identify the battlefield features and make the optimized force deployment in terms of the topography, the enemy status and our mission. A Relative Position Code method is presented to encode the deployment, which ensures the optimized formation will be kept after genetic operations. According to basic military principles, we propose a force deployment assessment scheme which evaluates a deployment solution from three aspects: unit fitness, formation fitness and relationship fitness. The DX-IFD system can quickly response to the change of battlefield situation, evaluate and create the optimized deployment in different configuration conditions. It greatly improves the force ability to quickly react and occupy the dominant positions in a battle.