Top 6 Most Popular Text Clustering Algorithms And How They Work

by | Jan 17, 2023 | Data Science, Machine Learning, Natural Language Processing

What exactly is text clustering?

The process of grouping a collection of texts into clusters based on how similar their content is is known as text clustering. Text clustering combines related documents that are easier to study or understand. Text clustering can be done using a variety of methods, including k-means clustering, hierarchical clustering, and density-based clustering. You can use these methods with different kinds of text data for different reasons.

What are the types of clustering

There are various clustering techniques, each with distinct advantages and disadvantages. The following list includes some of the most popular clustering methods:

  1. Centroid-based clustering: This type of clustering uses the mean or median of a cluster’s points as the cluster’s centre or centroid. K-means is the most popular centroid-based clustering algorithm.
  2. Hierarchical clustering: This type of clustering builds a hierarchy of clusters, where each cluster is a subset of the next higher-level cluster. There are two main types of hierarchical clustering: agglomerative and divisive.
  3. Density-based clustering: This type of clustering groups together points that are close to each other in the feature space. DBSCAN is the most popular density-based clustering algorithm.
  4. Distribution-based clustering: This type of clustering models the data as a mixture of probability distributions. The Gaussian Mixture Model (GMM) is the most popular distribution-based clustering algorithm.
  5. Spectral clustering: This type of clustering uses the eigenvectors of a similarity matrix to cluster the data.
  6. Neural network-based clustering: This type of clustering uses neural networks to learn the cluster structure of the data. Examples of this method are autoencoders and deep embedding clustering.
Neural networks to learn the text clustering structure of the data.

Neural networks to learn the cluster structure of the data.

Each method has its pros and cons and can be used depending on the nature of the data and what you want to accomplish.

Applications of text clustering

Text clustering is a powerful tool that can be applied to many applications. Some of the most common applications of text clustering include:

  1. Classifying text: Clustering can be used as a preprocessing step to put text documents into categories that have already been set up.
  2. Information retrieval: Clustering can be used to group similar documents together, making it easier to find relevant information.
  3. Text summarization: Clustering can be used to find the most representative or essential documents in a dataset, which can then be used to summarise the dataset’s content.
  4. Opinion mining and sentiment analysis: Clustering can group text documents expressing similar opinions or feelings.
  5. Topic modelling: Clustering can be used to find hidden topics in text documents, which can then determine how the data is organised.
  6. Language model improvement: clustering can be used to group text documents with similar topics or writing styles, which can then be used to improve language models.
  7. Marketing: Clustering can be used to group customer feedback, reviews, and survey responses to understand customer preferences, opinions, and feedback.
  8. Social Media Analysis: Clustering can be used to group social media posts, comments, and tweets, to understand the overall sentiment and opinions on a certain topic.

Text clustering is a flexible method that can be used in many situations and help get useful information out of large, complicated text datasets.

The best text clustering algorithm

1. K-means

A popular unsupervised learning algorithm for clustering is k-means. It is a straightforward, iterative algorithm that divides a dataset into k clusters, where k is a parameter that the user specifies. The fundamental goal of k-means is to define spherical clusters, with each cluster having a centroid (or centre point). The algorithm moves through two stages:

  1. Initialization: k initial centroids are randomly chosen from the data points.
  2. Iteration: Each data point is assigned to the cluster with the nearest centroid. After all the data points have been assigned, the centroids are recomputed as the mean of the points in the cluster.

This process of choosing and recalculating the centroid is repeated until the clusters stop changing or a certain stopping point is reached.

K-means has some flaws, including sensitivity to initial centroid placement and the presumption that all clusters are the same size and have a spherical shape. Additionally, it doesn’t account for the data density and struggles with categorical data.

Depending on the characteristics of the data and the desired result, other techniques like hierarchical clustering, DBSCAN, GMM, etc., may be helpful.

2. Hierarchical Clustering

A clustering technique called hierarchical clustering creates a hierarchy of clusters, each of which is a subset of the cluster above it. Two main categories of hierarchical clustering exist:

  1. Agglomerative: This is a “bottom-up” approach, where each data point is initially considered a single-point cluster and then combined with other clusters as the algorithm proceeds. The process stops when all the points are in one cluster or a stopping criterion is met.
  2. Divisive: This is a “top-down” approach, where all the data points are initially in one cluster, and the algorithm divides the cluster into smaller clusters. The process stops when each data point is in a separate cluster or a stopping criterion is met.

Agglomerative is the most common type of hierarchical clustering used.

Hierarchical clustering has some advantages over k-means, such as the ability to handle categorical data and the lack of the need to specify the number of clusters in advance.

However, because the number of clusters increases with the size of the data set and the output can be difficult to interpret, it can be computationally expensive for large datasets. It also might not be the best option for very large datasets. Additionally, the final clustering output can be impacted by the linkage method that is used, which is sensitive to that method.

3. DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that groups together points that are close to each other in the feature space. It differs from k-means and hierarchical clustering in that the number of clusters doesn’t have to be set up front, and it can find clusters of any shape.

The algorithm proceeds in two steps:

  1. Density-Reachability: Each point is assigned a density value based on the number of nearby points. Points with a high-density value are considered core points, and points with a low-density value are considered as noise points.
  2. Density-Connectivity: Core points are connected if they are within a certain distance (called the “epsilon” value) of each other. All reachable points from a core point are added to the same cluster.

DBSCAN has some advantages over k-means and hierarchical clustering: it can discover clusters of arbitrary shape, it can handle categorical data, it can discover clusters of varying densities, and it can identify noise points.

However, it has some flaws, such as the fact that selecting the right value of epsilon and minimum points can be difficult and sensitive, it can fail to discover dense enough clusters, and it can be sensitive to data scale.

4. Latent Semantic Analysis (LSA)

Latent Semantic Analysis (LSA) is a technique used to extract the underlying meaning or semantics of a set of text documents. It is based on the idea that words that are used in similar contexts tend to have similar meanings. LSA uses a mathematical technique called Singular Value Decomposition (SVD) to reduce the dimensionality of a term-document matrix, which represents the text data.

The basic steps in LSA are:

  1. Create a term-document matrix, where each row represents a term (word) and each column represents a document. The entries in the matrix are the term frequencies (or some other weighting) for each term-document pair.
  2. Perform SVD on the term-document matrix to obtain a low-rank approximation of the original matrix.
  3. Use the low-rank approximation to extract the latent semantic structure of the text data.

The result is latent semantics, which can be used to do things like classify texts, find information, and group texts.

LSA has some advantages, such as the ability to handle synonyms and polysemy, sparse data, and high dimensionality. However, it has some limitations too; it can’t handle the word order, it can’t handle the new words that are not in the training dataset, and it can also be sensitive to the choice of weighting scheme used.

5. Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA) is a generative probabilistic model that is commonly used for text clustering and topic modelling. It is based on the idea that each document in a set of documents is a mixture of a small number of latent topics, and each topic is a probability distribution over words in the vocabulary.

The basic steps in LDA are:

  1. Create a prior for the latent topics.
  2. For each document, sample a topic mixture from the prior.
  3. For each word in the document, sample a topic assignment from the topic mixture.
  4. For each topic, estimate the word probabilities based on the topic assignments.

The topic-word probabilities and document-topic proportions that come out of this can be used to do things like classify documents, find information, and summarise text.

LDA has some advantages, like the fact that it can discover latent topics that might not be evident from the text, it can handle large datasets, and it can handle sparse data. However, it has some limitations too. It assumes that the number of topics is known in advance, and it also assumes that the topics are fixed and not overlapping. It can also be affected by the prior, the number of topics, and the number of iterations used.

6. Neural network based clustering

Neural network-based clustering is a type of clustering that uses neural networks to learn the cluster structure of the data. This method works best with data that has a lot of dimensions and is complicated, like images or text.

There are several neural network-based clustering methods, including:

  1. Autoencoder: An autoencoder is a neural network trained to reconstruct its inputs. The bottleneck layer of the autoencoder can be used as a low-dimensional feature representation of the data, which can then be clustered using traditional methods such as k-means.
  2. Deep Embedding Clustering (DEC): DEC is an algorithm that uses deep neural networks to learn a low-dimensional feature representation of the data and then applies k-means clustering on the learned features.
  3. Generative Adversarial Networks (GANs): GANs are a class of neural networks trained to generate new data samples similar to the training data. Clustering can be done with GANs by teaching a GAN to generate data from each cluster and then using the generator to put new data points in the cluster that is closest to them.
  4. Variational Autoencoder (VAE): A VAE is a generative model that learns a probabilistic encoder-decoder. The encoder learns a compact representation of the input data, and the decoder generates data from the compact representation. It can be used for clustering by training the VAE on data from different clusters and using the encoder to assign new data points to the closest cluster.

These neural network-based methods have been shown to be effective on several datasets and can be used as an alternative to traditional clustering methods. However, they also have some limitations, like the fact that they can be computationally expensive and that they require a large amount of data to learn the cluster structure.

Challenges of text clustering

Text clustering is a challenging task due to the nature of text data and the complexity of natural language. Some of the main challenges in text clustering include:

  1. High dimensionality: Text data is often represented as a high-dimensional sparse matrix, making it hard to use traditional clustering algorithms.
  2. Noise and outliers: Text data can have noise like misspellings, typos, and irrelevant information, making it hard to find patterns that mean something.
  3. Categorical data: Text data is often categorical, which means it doesn’t have a natural sense of distance or similarity.
  4. Handling synonyms and polysemy: Words in text data often have more than one meaning, making it hard to figure out the real meaning.
  5. Handling sparse data: Text data is often sparse, meaning many words don’t appear in most documents. This makes it hard to find patterns in the data that mean something.
  6. Handling large datasets: Clustering large datasets can be computationally expensive and require a large amount of memory.
  7. Handling new words: Clustering algorithms are trained on a fixed dataset, so they may not be able to handle new words in the training dataset.
  8. Scalability: Clustering algorithms should scale well with increasing data size; otherwise, they can become impractical to use with large datasets.
  9. Evaluation: Clustering results are hard to evaluate since there is no single correct answer for clustering and the evaluation metric depends on the application and the dataset.

Despite these challenges, text clustering is still a valuable technique for extracting insights from text data. As a result, it can be used in a wide range of applications. Researchers are coming up with new methods and algorithms to deal with these problems. So new deep learning-based methods can handle the complexity of text data.

How to cluster text and numeric data

One of the most frequently asked questions is how to mix text and numbers in a clustering task. This can be hard because the two data types have different qualities and are usually handled differently. One way to combine text and numbers is first to use a method like term frequency-inverse document frequency (TF-IDF) or latent semantic analysis (LSA) to find numerical features in the text data. Then, you can use these numerical features along with the numbers to cluster.

Another approach is to perform clustering separately on the text and numeric data and then combine the results. For example, you can cluster the text data using a technique such as latent dirichlet allocation (LDA) and cluster the numeric data using a technique such as k-means. Then, you can use the cluster labels from the text data as additional features in the numeric data clustering, or vice versa.

Deep learning techniques such as autoencoders can also be used to cluster the encoded feature space.

It’s also important to remember that combining text and numbers in clustering might require more computing power and memory. The results should be carefully evaluated and interpreted.

Conclusion

In conclusion, clustering is a good way to look through and understand large, complicated datasets. There are numerous varieties of clustering techniques, each with unique advantages and disadvantages.

Although simple and popular, centroid-based methods like k-means can be sensitive to initial conditions and assume spherical clusters.

Although it can handle non-spherical clusters, hierarchical clustering can be computationally expensive. DBSCAN and other density-based methods can find clusters of any shape, but they can be sensitive to parameter selection.

Techniques based on neural networks and distributions, such as the Gaussian Mixture Model (GMM), Deep Embedding Clustering, Generative Adversarial Networks, and Variational Autoencoder, are both effective but computationally expensive.

The characteristics of the data and the desired result will ultimately determine the technique to use. Also, see our article on document clustering on how to implement these techniques.

What clustering have you used in your projects?

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