other
所属分类：人工智能/神经网络/深度学习
开发工具：Visual C++
文件大小：245KB
下载次数：28
上传日期：2012-06-06 10:40:14
上 传 者：
986663233
说明： 层次聚类，层次聚类分析，VC++代码，简单易懂，可以运行
(Hierarchical clustering, hierarchical clustering analysis, VC++ code, easy to understand, you can run)
文件列表:
other (0, 2012-06-06)
other\add-blank.c (709, 2012-06-06)
other\add-blank.dsp (3437, 2012-06-06)
other\add-blank.dsw (526, 2012-06-06)
other\add-blank.ncb (33792, 2012-06-06)
other\add-blank.opt (48640, 2012-06-06)
other\add-blank.plg (1291, 2012-06-06)
other\alloc.c (3169, 2002-01-21)
other\alloc.h (1890, 2002-01-21)
other\cluster-no-noise.c (27460, 2002-01-21)
other\cluster-no-noise.h (627, 2002-01-21)
other\cluster-orig.c (18421, 2002-01-21)
other\cluster-sim.c (19675, 2002-01-21)
other\cluster.c (24935, 2002-01-21)
other\cluster.h (610, 2002-01-21)
other\Debug (0, 2012-06-06)
other\Debug\add-blank.exe (208968, 2012-06-06)
other\Debug\add-blank.ilk (206684, 2012-06-06)
other\Debug\add-blank.obj (4163, 2012-06-06)
other\Debug\add-blank.pch (175496, 2012-06-06)
other\Debug\add-blank.pdb (402432, 2012-06-06)
other\Debug\vc60.idb (33792, 2012-06-06)
other\Debug\vc60.pdb (45056, 2012-06-06)
other\error.c (447, 2002-01-21)
other\error.h (1585, 2002-01-21)
other\genplots-noise.c (1912, 2002-01-21)
other\genplots.c (1874, 2002-01-21)
other\graph.c (10763, 2002-01-21)
other\Makefile (4220, 2002-01-21)
other\pca.c (10929, 2002-01-21)
other\testm.c (909, 2002-01-21)
other\tmp.c (1861, 2002-01-21)
0. I have modified an existing hierarchical clustering code and followed
the CURE paper as closely as possible. Please let me know (han@cs.umn.edu)
if you find any bugs in the code.
1. First part of the input file (ex1.dat) is shown below. Each row corresponds
to (x,y) coordinates of a data point.
40.678 63.***31
41.4301 ***.8066
41.3468 63.8069
42.3465 63.7236
41.93 62.9738
40.7637 62.8905
2. You compile and run the code as follows:
% cluster -k 2 -a 0.1 -r 10 ex1.dat
Note that -k option is for the number of clusters, -a is for alpha
parameter of CURE, and -r is the number of representative points of
the cluster.
The above run will generate 2 clusters with alpha 0.1 and the number
of representative points 10.
3. This will generate ex1.dat-partition file like following (note that this
is not an output from the real run):
0
0
1
1
1
0
The first 2 data points and the last data point belongs to cluster 0 and
the remaining 3 data points belong to cluster 1.
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