本文探讨了文本挖掘中两种常用的模式识别方法：主题建模和聚类分析。主题建模通过计算词语在主题中的条件概率，识别文档中隐藏的主题，并将其表示为一组关键词及其概率。聚类分析则根据文档之间的相似度，将文档划分为不同的簇。文章重点阐述了两种方法的区别，主题建模将数据转化为更小的主题空间，而聚类分析则将文档分组。通过对比，读者可以更好地理解两种方法的适用场景，从而选择合适的文本挖掘工具进行数据分析。

🤔**主题建模**：通过计算词语在主题中的条件概率，识别文档中隐藏的主题。每个主题由一组关键词及其概率构成，一个文档可能包含多个主题。

📊**聚类分析**：将文档转换为数值向量，并根据相似度度量将文档划分为不同的簇。通常每个文档只属于一个簇，但也有模糊聚类方法允许文档属于多个簇。

💡**主题建模与聚类分析的区别**：主题建模将数据转化为更小的主题空间，而聚类分析则将文档分组，最终输出的是簇及其成员。

📚**关键词权重**：在聚类分析中，可以使用TF-IDF等方法为文档中的词语分配权重，构建数值向量。

🔍**应用场景**：根据具体需求选择合适的文本挖掘方法，主题建模适用于发现隐藏主题，聚类分析适用于将文档分组。

Alright, so you have a huge pile of documents and you want to find mysterious patterns you believe are hidden within! A challenging task, but you are lucky because you have WordStat in your arsenal. That means, you have at least two options: 1) topic modeling, or 2) cluster analysis to find patterns and groupings in your data. But which one you should use?  In this post, we will help you choose by highlighting some of the differences between topic modeling and clustering approaches.

As we explained in our previous post about topic modeling, a topic can be defined by a set of keywords with each keyword in the set having a probability of occurrence for the subject topic. Different topics have their own sets of keywords with corresponding probabilities and topics may share some keywords, but most likely with different probabilities. Easy, right?! Oh, I forgot to mention that a document in your corpus can be associated with more than one topic. There are a wide set of different approaches to choose for discovering hidden topics, but in general, in topic modeling we deal with uncovering the topics through calculating conditional probabilities of the topics given the words in the documents. No matter what approach you select, in topic modeling you will end up with a list of topics, each containing a set of associated keywords.

Things are slightly different in clustering! Here, the algorithm clusters documents into different groups based on a similarity measure. One way would be transforming the documents to a numeric vector containing the weights assigned to words in the given document. I know you are thinking about tf-idf, yes this is one way to do it. The clustering technique applies the similarity measure to the numeric vectors to group the documents. Basically, each document will show up in one cluster[1]. The final output would be a list of clusters along with their members.

Okay, what’s the difference then? Well, in topic modeling you extract topics out of the documents, so you may think about it like transforming to a much smaller data space, that is the topic space, since the number of the extracted topics is much less than the document collection and its vocabulary. In (hard) clustering, the final output contains a set of clusters each including a set of documents.

 

[1] There are fuzzy (soft) clustering techniques where a data point can belong to more than one cluster, but here we discuss the basic idea and we only focus on hard clustering.