基于DBSCAN的集群字符串

Question

摘要：在基于“目录”列对多列csv文件进行聚类中寻找python代码的DBSCAN实现]

Input:

    input csv file rows sample

    Rank, Domain, Contents      

    1, abc.com, hello random text out
    2, xyz.com, hello random somethingelse
    3, not.com, a b c d
    4, plus.com, a b asdsadsa asdsadasdsadsa
    5, minus.com, man win 

   Where,

   Column 1 => Rank = digit
   Column 2 => Domain = domain name ex. abc.com
   Column 3 => Contents = list of words (string, this is 
extracted clean up words from html page)

Output :

    The output of the cluster be based on similar list of contents

    Cluster 1: abc.com, xyz.com
    Cluster 2: not.com, plus.com
    Cluster 3: minus.com
    ....

    Please note: In output, I am not looking for words that are in same cluster. Instead, I am looking for a 'domain name', column which is clustered based on similar contents of column 3, 'contents' 

我研究了以下资源，但它们基于kmeans，与我要寻找的DBSCAN集群输出无关。请注意，由于我们不想根据输入来限制群集号，因此提供群集号在这种情况下将不适用。

1）How can I cluster text data with multiple columns?

2）Clustering text documents using scikit-learn kmeans in Python

3）http://brandonrose.org/clustering

4）https://datasciencelab.wordpress.com/2013/12/12/clustering-with-k-means-in-python/

5）https://towardsdatascience.com/applying-machine-learning-to-classify-an-unsupervised-text-document-e7bb6265f52

so，

input <= csv file with 'Rank', 'Domain', 'Contents'
output <= cluster with domain name [NOT contents]

A python implementation in DBSCAN clustering would be an ideal.

谢谢！

Answer 1

您首先需要选择数据集的“内容”列。您可以在该步骤中使用Python的csv模块。

然后，您必须将文本转换为可以在其上训练DBSCAN的向量。您提供的第二个链接包含执行该步骤所需的一切。

然后您必须在向量上训练DBSCAN。例如，您可以使用DBSCAN in scikit-learn的实现。

一旦有了与向量关联的标签（即csv文件的行），就可以按簇对行数进行分组并检索域。