DBScan-master
说明： 这是使用Python实现的DBScan。像Numpy、熊猫这样的图书馆也被使用过。DBScan算法已经在两个变色龙数据集t4.8k和t5.8k上进行了测试。然后利用matplotlib将得到的结果可视化。为了便于比较，本文将所得到的输出结果与DBScan实现的skLearning库的结果进行了比较。计算每个数据集的同质性和分离度，以观察簇间的相似性和不同的度量。epsilon和min值分别为8.5和16.5。
(This is a DBScan implemented using Python. Libraries like Numpy and Panda have also been used. DBScan algorithm has been tested on two chameleon datasets t4.8k and t5.8k. Then the results are visualized by matplotlib. In order to facilitate comparison, the output results are compared with those of skLearning library implemented by DBScan. The homogeneity and segregation of each data set are calculated to observe the similarity and different measures between clusters. Epsilon and min were 8.5 and 16.5 respectively.)
(This is a DBScan implemented using Python. Libraries like Numpy and Panda have also been used. DBScan algorithm has been tested on two chameleon datasets t4.8k and t5.8k. Then the results are visualized by matplotlib. In order to facilitate comparison, the output results are compared with those of skLearning library implemented by DBScan. The homogeneity and segregation of each data set are calculated to observe the similarity and different measures between clusters. Epsilon and min were 8.5 and 16.5 respectively.)
文件列表:
DBScan-t4.8k.ipynb (800658, 2019-05-17)
DBScan-t5.8k.ipynb (706593, 2019-05-17)
Screens (0, 2019-05-17)
Screens\Result on t4.8k.png (169313, 2019-05-17)
Screens\Result on t5.8k.png (136251, 2019-05-17)
t4.8k.csv (180919, 2019-05-17)
t5.8k.dat (169672, 2019-05-17)
DBScan-t5.8k.ipynb (706593, 2019-05-17)
Screens (0, 2019-05-17)
Screens\Result on t4.8k.png (169313, 2019-05-17)
Screens\Result on t5.8k.png (136251, 2019-05-17)
t4.8k.csv (180919, 2019-05-17)
t5.8k.dat (169672, 2019-05-17)
# DBScan
This is an implementation of DBScan using Python. Libraries such as numpy, pandas have been used. The DBScan algorithm has been tested on two chameleon datasets - t4.8k and t5.8k. The results obtained are then visualised with the help of matplotlib. For comparision purposes, the output obtained is compared with the results from DBScan implementation of sklearn library. Homogeneity and Separation for each of the datasets are computed in order to observe similarity and dissimilarity measures between the clusters. The values of epsilon and min_points have been taken as 8.5 and 16.5 respectively.
## Screenshots:
# Result on t4.8k
# Result on t5.8k
# Result on t5.8k
近期下载者：
相关文件：
收藏者：