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Unsupervised Learning with Text Clustering: An Exploration of Algorithms and Approaches

Aleksandra Tadrzak

8 min read

Jan 11, 2024

Every human activity generates data. Despite the differences in complexity, a slip of paper containing a restaurant order and a detailed form containing the results of a medical test have one thing in common: they both contain valuable text data.

For businesses, success lies in how effectively they can process the data generated through their activities. Data comes in many forms, from numerical to multimedia. Text data is one of its most common forms, and one of the best ways to process it is via text clustering.

Text clustering breaks down text data into groups based on shared attributes. Objects in the same cluster will have similar qualities, while objects in different clusters will have dissimilar qualities. In text data analysis, objects can be individual words, incomplete phrases, complete sentences, or entire documents. It all depends on the nature of the data analysis being performed.

Unsupervised learning is one of the methods by which computer programs learn how to perform text clustering. In this article, we will explore how unsupervised learning works in relation to text clustering and the different methods used to complete it. We will also shed light on the different use cases for unsupervised learning and text clustering.

What is unsupervised learning?

The development of artificial intelligence (AI) and machine learning (ML) have had a major impact on data analysis. It’s now possible to outsource analyzing large volumes of data to computer programs and algorithms. There are two ways that programs become proficient at data analysis: supervised learning and unsupervised learning.

Our focus is on unsupervised learning, and the best way to understand how it works is by comparing it to its counterpart, supervised learning. In supervised learning, human operators provide the ML program with training data that it uses to reference any new data. The program will sort all the data into predetermined categories in a process known as classification.

Meanwhile, in unsupervised learning, the program does not have any training data. Starting from a blank canvas, it uses information within the data to identify similarities between objects and create clusters. Instead of a fixed number of preset categories, this approach sorts data into a variable number of groups based on its findings, known as clustering.

What is text clustering?

Text clustering is the result of applying unsupervised learning programs to textual data. Separating disparate objects into groups based on lexical or semantic similarity is at the heart of text clustering. The number of groups, or clusters, is never fixed. It depends on the clustering method being used and can also change depending on the analysis exercise’s aims.

There are two main kinds of clustering methods.

Hard clustering: In this method, every object belongs to no more than a single cluster.
Soft clustering: In this method, objects can belong to more than one cluster depending on their values. Their membership in these soft clusters is partial, which means they will fit in better with one cluster but still share some values with the other clusters they are in.

The above methods rely on the partition of objects. But, there is another method of text clustering known as hierarchical clustering.

Hierarchical clustering groups individual objects into clusters and then iteratively groups clusters together into super-clusters. These super-clusters are ultimately combined into the same group, which is the root. While other clustering methods are represented as graphs or charts, hierarchical clustering is represented with a dendrogram (tree-shaped flowchart).

The two subcategories of hierarchical clustering are:

Agglomerative: Starts from the bottom-up, beginning with individual clusters and unifying them until arriving at the root.
Divisive: A top-down method that starts with the unified root and then goes on to separate clusters until only individual objects remain.

Whether you use partitional or hierarchical methods, text clustering is a reliable way to find insights into large bodies of textual data.

How text clustering works

Now that we understand text clustering let’s examine how it works.

Successful, effective text clustering isn’t a simple affair. You can’t just apply your preferred algorithm to a body of data and end up with useful, insightful clusters. A more complete understanding of the process is required to enjoy its benefits.

Text clustering aims to represent a textual document numerically, with its components represented as vectors of features. The distance between two objects’ feature vectors is then used to determine their similarity. As we know, objects with feature vectors that are close together belong in the same cluster, and those that have more distance between them go into different clusters.

There are four key stages in text clustering.

Text pre-processing

Before beginning, the textual data must be prepared for analysis. This process involves tokenization, transformation, and the removal of “stop words'' with little meaning.

Feature extraction

At this point, the frequency of a word or token’s appearance in the data is calculated, resulting in features for each of them. You need to select which features you will look at for your analysis.

Clustering

The generated features are then grouped into clusters based on the distance between them. This is accomplished by choosing a suitable clustering method and algorithm.

Final evaluation

This is the stage where the resulting clusters are examined for useful information. Since text clustering is an unsupervised process, the results may be unsatisfactory, making it necessary to start again using a different method and algorithm.

Three of the most popular text clustering methods

Text clustering is a flexible approach to data analysis, and there are many viable methods to use it. We will look at some of the most widely used ones below.

K-means

This is a two-stage process that uses an unsupervised learning algorithm. It divides text data into K-clusters, which are clusters centered around a K-point specified by the analyst. These K-points serve as center points for each cluster and will shift around as the algorithm processes data.

The two stages of the K-means approach are:

Initialization: Where the initial K-points are chosen.
Iteration: Data points are assigned to K-clusters based on their proximity to K-points. Every time a new data point is assigned to a K-cluster, its K-point is recomputed according to the mean position of all the points in the cluster.

DBSCAN

The difference between Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and K-means is that DBSCAN does not need K-points to be determined in advance. It assigns its own core points by selecting high-density points in the feature space. Data points occurring in low-density areas are considered noise.

DBSCAN is ideal for finding clusters of an irregular shape based on their proximity to high-density feature spaces. The two steps of the DBSCAN algorithm are:

Density reachability: Every data point is assigned a density value calculated according to the number of nearby points.
Density connectivity: Points that are within a predetermined distance (epsilon value) of a core point are added to that cluster.

Neural network-based clustering

When text data is unstructured or accompanied by numbers or images, neural network-based clustering is the most effective. The different methods of neural network-based clustering include:

Autoencoder
Variational Autoencoder (VAE)
Deep Embedding Clustering (DEC)
Generative Adversarial Network (GAN)

Each one of these methods has its pros and cons, which are worth looking into before choosing one for text clustering purposes.

Pros and cons of using text clustering

Text clustering is a valuable tool for businesses and can be used for multiple purposes, such as document retrieval, customer feedback analysis, and content filtering. However, like any form of data science, it has its pros and cons.

Benefits of text clustering

Text clustering is one of the easiest ways to understand large, complicated datasets. Each clustering technique comes with unique features and insights; knowing how to apply those to different datasets is essential.

Text clustering, powered by unsupervised learning, lets businesses identify patterns in their database much faster than manual approaches, allowing them to respond to trends faster.

Challenges of text clustering

Some of the challenges of successfully applying text clustering in data analysis include:

Choosing the appropriate method for a dataset,
Handling rare and novel words in documents,
Computational expenses for handling large datasets,
Identifying noise and irrelevant information in text data.

Examples of unsupervised learning and text clustering in action

Many businesses have turned to text clustering for analytical purposes in today's data-driven world. Some examples of unsupervised learning working in conjunction with text clustering are:

Google uses text clustering to determine which pages are most relevant to its search results.
Amazon applies text clustering to customer reviews to make better recommendations about products.

Text clustering for customer service

From helping streamline complaint resolution processes to providing sentiment analysis based on social media mentions and online reviews, text clustering is an invaluable tool for customer service teams.

Services like Text provide advanced analytics, including text clustering, that help businesses enrich their customer service offerings. Using text clustering, you can convert your business’ raw data into valuable knowledge!

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