Density Peaks Clustering (DPC) is a well-known clustering technique in the data mining field with fewer parameters as well as no iteration. However, when dealing with datasets containing multiple peaks, DPC may subjectively choose the wrong ...

The Density Peaks Clustering (DPC) algorithm is simple and efficient, but still has a few limitations. For example, DPC needs manual selection of clustering centers and may miss the correct cluster when searching for denser nearest neighbors, ...

Density peaks clustering (DPC) algorithm has been widely applied in many fields due to its innovation and efficiency. However, the original DPC algorithm and many of its variants choose Euclidean distance as local density and relative distance ...

Spiking neural P systems (SNP systems) are parallel, distributed, low energy consuming, non-deterministic, interpretable and simple structured computational models abstracted from biological neural systems. In this work, to make SNP ...

• LTPD-SNP systems is proposed, which boosts spiking neural P systems' learning ability and intelligence.

A kernel-based intuitionistic weight fuzzy k-modes algorithm (KIWFKM) is proposed in this paper which can improve the clustering performance of categorical data. The current FKM algorithm which were proposed by researchers generally ...

In recent times, a density peaks based clustering algorithm (DPC) that published in Science was proposed in June 2014. By using a decision graph and finding out cluster centers from the graph can quickly get the clustering results, easy and ...

Inspired by the P system with dynamic membrane structure and the mechanism of DNA genetic information, an optimization algorithm based on improved DNA Genetic Algorithm (DNA-GA) and the cell-like P system with dynamic membrane structure called DNA-...

Bio-inspired algorithms for optimization are significant topics in the areas of computational intelligence. Traditional genetic algorithm easily gets stuck at a local optimum, and often has slow convergent speed. To overcome these drawbacks, the Cloud ...

Membrane computing is a new branch of natural computing. In recent years, with the rapid development, membrane systems are divided into three categories: Cell-type membrane systems, Tissue membrane system, Neuronal membrane system. Spiking neural ...

The current intense market competition environment force many enterprises take more and more attention to customer demands, and adopt effective methods to evaluate the importance of customer satisfaction. In order to analysis the customer's actual need, ...

A novel DNA encoding genetic algorithm, called SVM-DNAGA, is proposed to search for optimal values for the parameters in support vector machines. With this algorithm, the training process of support vector machines can converge quickly and the ...

This paper proposed a k-means clustering analysis method, which is based on DNA genetic algorithm and P system. DNA encoding is used to analyze the initial center of cluster and the P system is used to realize the clustering. The quality of the ...

In this paper, artificial neural network is used to establish an unified model of DNA computing to solve their classification. The main feature of this model is that the idea it uses is parallel logic completely different from traditional computer ...

At present the three-dimensional visualization technology is increasingly mature, CAD technology development is playing a more and more important role, especially in the three-dimensional graphics configuration. This paper aims to embed Google ...

This article proposes a fuzzy C-means clustering analysis method, which is based on DNA genetic algorithm. DNA encoding is used to analyze the center of the cluster and the quality of clustering is judged by eigenvectors and the sum of Euclidean ...