Description: Matlab provides two methods of cluster analysis. One is a function of the sample data using clusterdata to conduct a cluster, its disadvantages for the user to select a narrower face and can not change the method of calculating the distance the other is the sub-Buju categories: (1) find a data set variable pairwise similarity between the non-similarity, calculated using pdist function, the distance between variables (2) function definition with the linkage connection between variables (3) with the cophenetic clustering evaluation function, the information (4) with cluster function is to create clustering.
- [basic_method] - test1.m fixed incremental method for use
- [dynimcCluster] - Matlab realize the dynamic clustering al
- [cluster-2.9] - ClustanGraphics clustering analysis tool
- [clusteranalysis] - Simple and useful software for cluster a
- [2-3-1] - Matlab cluster analysis algorithm to ach
- [collectAnaly] - collectAnaly
- [kmean] - Including the K-means clustering algorit
- [K-means] - Cluster with matlab to achieve good resu
File list (Check if you may need any files):
iriscode\createiristemplate.m
........\Matching\gethammingdistance.m
........\........\shiftbits.m
........\Normal_encoding\encode.m
........\...............\gaborconvolve.m
........\...............\normaliseiris.m
........\README.txt
........\Segmentation\addcircle.m
........\............\adjgamma.m
........\............\canny.m
........\............\circlecoords.m
........\............\findcircle.m
........\............\findline.m
........\............\houghcircle.m
........\............\hysthresh.m
........\............\linecoords.m
........\............\nonmaxsup.m
........\............\segmentiris.m
........\Matching
........\Normal_encoding
........\Segmentation
iriscode
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