文件丛集基础 [英] Document Clustering Basics

问题描述

因此，我已经考虑了这些概念一段时间了，我的理解是非常基础的.信息检索似乎是一个很少涉及的话题...

So, I've been mulling over these concepts for some time, and my understanding is very basic. Information retrieval seems to be a topic seldom covered in the wild...

我的问题来自文档聚类的过程.假设我从一组仅包含有趣单词的文档开始.第一步是什么?解析每个文档中的单词并创建一个巨大的单词袋"类型模型?然后，我是否要继续为每个文档创建单词计数向量?如何使用K-means聚类比较这些文档?

My questions stem from the process of clustering documents. Let's say I start off with a collection of documents containing only interesting words. What is the first step here? Parse the words from each document and create a giant 'bag-of-words' type model? Do I then proceed to create vectors of word counts for each document? How do I compare these documents using something like the K-means clustering?

推荐答案

尝试 Tf-idf 对于初学者.
如果您阅读Python，请查看 使用MiniBatchKmeans聚类文本文档" 在 scikit-learn 中:
一个示例，展示了如何使用scikit-learn进行集群 文档使用主题词袋方法".
然后，源代码中的feature_extraction/text.py具有非常好的类.

Try Tf-idf for starters.
If you read Python, look at "Clustering text documents using MiniBatchKmeans" in scikit-learn:
"an example showing how the scikit-learn can be used to cluster documents by topics using a bag-of-words approach".
Then feature_extraction/text.py in the source has very nice classes.

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