An Improved Projection Pursuit Clustering Model and its Application Based on Quantum-behaved Particle Swarm Optimization
نویسندگان
چکیده
Extracting the information with biological significance in amounts of gene expression data is an important research direction. Clustering algorithm in this area has been increasingly widely applied. According to the characteristic of gene expression data, the improved projection pursuit cluster model was introduced in this area and Quantum-behaved Particle Swarm Optimization(QPSO) was put forward to find the optimal projection direction. The simulation results showed that the improved strategy was feasible and effective. This method was not only a new way for the massive high-dimensional data clustering, but also provided a new approach for the cluster analysis of gene expression data. Keywords—QPSO; projection pursuit; gene expression data; clustering
منابع مشابه
OPTIMUM SHAPE DESIGN OF DOUBLE-LAYER GRIDS BY QUANTUM BEHAVED PARTICLE SWARM OPTIMIZATION AND NEURAL NETWORKS
In this paper, a methodology is presented for optimum shape design of double-layer grids subject to gravity and earthquake loadings. The design variables are the number of divisions in two directions, the height between two layers and the cross-sectional areas of the structural elements. The objective function is the weight of the structure and the design constraints are some limitations on str...
متن کاملEmpirical Study on How to Set Prices for Cruise Cabins Based on Improved Quantum Particle Swarm Optimization
This essay puts forward a cruise pricing model based on improved quantum particle swarm optimization, aiming at optimizing the pricing strategy and realizing the maximum sales income expected. Firstly, we combine the two factors – actual booking records and expected booking records in the process of cruises pricing – and improve the dynamic price-setting model based on demand learning put forwa...
متن کاملAdaptive Parameter Selcetoin of Quantum-behaved Particle Swarm Optimization on Global Lebvel
In this paper, we formulate the dynamics and philosophy of Quantum-behaved Particle Swarm Optimization (QPSO) Algorithm, and suggest a parameter control method based on the whole population level. After that we introduce a diversity-guided model into the QPSO to make the PSO system an open evolutionary particle swarm and therefore propose the Adaptive Quantum-behaved Particle Swarm Optimization...
متن کاملImproved Quantum-Behaved Particle Swarm Optimization
To enhance the performance of quantum-behaved PSO, some improvements are proposed. First, an encoding method based on the Bloch sphere is presented. In this method, each particle carries three groups of Bloch coordinates of qubits, and these coordinates are actually the approximate solutions. The particles are updated by rotating qubits about an axis on the Bloch sphere, which can simultaneousl...
متن کاملAn Improved Quantum-behaved Particle Swarm Optimization Algorithm Based on Chaos Theory Exerting to Particle Position
In this paper, we propose an improved quantum-behaved particle swarm optimization (QPSO), introducing chaos theory into QPSO and exerting logistic map to every particle position X(t) at a certain probability. In this improved QPSO, the logistic map is used to generate a set of chaotic offsets and produce multiple positions around X(t). According to their fitness, the particle's position is upda...
متن کامل