A Novel Hybrid Clustering Algorithm Incorporating K-Means into Canonical Immune Programming Algorithm

2010 ◽  
Author(s):  
Xiao-shuai Xing ◽  
Zhu Li ◽  
Qing-quan Zhang ◽  
Pei-lin Yang ◽  
Jian-bin Yao
Keyword(s):  
Clustering Algorithm ◽  
Programming Algorithm ◽  
Hybrid Clustering ◽  
Immune Programming

Improved Clustering Algorithm of Spatial Data Structure Based on Matlab Simulink

2014 ◽  
Vol 608-609 ◽  
pp. 98-102
Author(s):  
Shan Mei Xiong ◽  
Ru Lian Wu ◽  
Hui Wang
Keyword(s):  
Spatial Data ◽  
Clustering Algorithm ◽  
Geographical Location ◽  
Calculation Model ◽  
Space Structure ◽  
Urban Tourism ◽  
Programming Algorithm ◽  
Tourism Destination ◽  
Consumption Structure ◽  
Matlab Programming

This paper has introduced the clustering algorithm into the model of urban tourism destination consumption structure, and has used MATLAB programming algorithm to improve the calculation model of consumption structure for tourism destination, which has obtained the spatial data model of the consumption structure. The model roundly considers the influence of geographical location, cultural factors, political factors and economic factors, and it establishes new clustering algorithm model with four coefficients, and has realized the algorithm by the use of MATLAB programming. Finally, the consumption structure of the same destination in different provinces is calculated by using the spatial system model, which has obtained the calculation curve of consumption space structure and the clustering results, and has provided technical reference for the research on consumption of urban tourism destination.

Analysis of Sparse Representation and Blind Source Separation

2004 ◽  
Vol 16 (6) ◽  
pp. 1193-1234 ◽  
Author(s):  
Yuanqing Li ◽  
Andrzej Cichocki ◽  
Shun-ichi Amari
Keyword(s):  
Sparse Representation ◽  
Blind Source Separation ◽  
Clustering Algorithm ◽  
Source Separation ◽  
Coefficient Matrix ◽  
Data Matrix ◽  
Programming Algorithm ◽  
Optimization Approach ◽  
Basis Matrix ◽  
Two Stage

In this letter, we analyze a two-stage cluster-then-l1-optimization approach for sparse representation of a data matrix, which is also a promising approach for blind source separation (BSS) in which fewer sensors than sources are present. First, sparse representation (factorization) of a data matrix is discussed. For a given overcomplete basis matrix, the corresponding sparse solution (coefficient matrix) with minimum l1 norm is unique with probability one, which can be obtained using a standard linear programming algorithm. The equivalence of the l1—norm solution and the l0—norm solution is also analyzed according to a probabilistic framework. If the obtained l1—norm solution is sufficiently sparse, then it is equal to the l0—norm solution with a high probability. Furthermore, the l1—norm solution is robust to noise, but the l0—norm solution is not, showing that the l1—norm is a good sparsity measure. These results can be used as a recoverability analysis of BSS, as discussed. The basis matrix in this article is estimated using a clustering algorithm followed by normalization, in which the matrix columns are the cluster centers of normalized data column vectors. Zibulevsky, Pearlmutter, Boll, and Kisilev (2000) used this kind of two-stage approach in underdetermined BSS. Our recoverability analysis shows that this approach can deal with the situation in which the sources are overlapped to some degree in the analyzed

An efficient hybrid clustering algorithm for segmentation: Autocluster

2017 ◽  
Vol 2 (3) ◽  
pp. 205
Author(s):  
Seyed Behnam Khakbaz ◽  
Marziyeh Pourestarabadi ◽  
Nastaran Hajiheydari
Keyword(s):  
Clustering Algorithm ◽  
Hybrid Clustering
Sign in / Sign up

Export Citation Format

Share Document