Evaluate Data efficiently with Unsupervised Learning

The beginnings of unsupervised learning go back to the 1960s. At that time, companies began to introduce clustering algorithms to categorize their data. In the 90s, methods such as principal component analysis were added to support the analysis of further increasing data volumes.

In the early 2000s, the advent of Big Data quickly showed that previous methods were no longer sufficient to efficiently generate real added value from data. Rather, companies needed techniques with which they could also make predictions for their processes. In this context, AI-based Unsupervised Learning has become an elementary part of data analysis. We will show you how Unsupervised Learning works, how it differs from related techniques and how you and your company can benefit from it in practice. 

The most Important in a Nutshell

  • Unsupervised learning recognizes patterns and structures in unlabeled data without prior guidance.
  • Areas of application for unsupervised learning include natural language processing (NLP) and quality control.
  • Konfuzio is your contact for the automated extraction and evaluation of unlabeled data from documents.
unsupervised learning definition

Unsupervised Learning - Definition

Unsupervised learning is an approach in Machine Learningin which an algorithm recognizes patterns and structures in data - without prior guidance or examples. In contrast to supervised learning and semi-supervised learning, algorithms in unsupervised learning learn exclusively from unlabeled data - i.e. data that is not labeled with features, properties or classifications.

Unsupervised learning attempts to detect patterns in input data that are different from structureless noise. For this purpose, there are various methods such as cluster analysis, association rules, and dimensionality reduction.

Companies use unsupervised learning for various application areas. For example, they use it to identify similar groups of data points, discover hidden structures in data, and find new criteria for categorizations. What this means:

Unsupervised learning enables processes to be designed more efficiently and more informed decisions to be made in a business context.

In practice, the technology is used in areas such as image recognition, the Speech Processing and anomaly detection are used.

Unsupervised Learning vs. Supervised Learning 

Unsupervised learning and supervised learning are two important approaches in machine learning. Unsupervised learning focuses on discovering patterns in data, without prior guidance. It does not require labeled examples to do so. This is because: the model learns structures and relationships in the data on its own.

In contrast, supervised learning uses labeled data to make predictions. The model learns from existing examples and is therefore able to classify or predict new, unlabeled data. To do this, companies must provide the model with clear instructions in the form of input-output pairs.

Another difference between supervised and unsupervised learning is that companies use unsupervised learning for clustering and dimension reduction, while they use supervised learning mainly for classification and regression. However, both approaches are valuable tools in machine learning to efficiently leverage the value of data.

Unsupervised Learning vs. Semi-supervised Learning

Unsupervised learning and semi-supervised learning are two paradigms in machine learning that differ in the way they handle labeled and unlabeled data.

While unsupervised learning algorithms learn exclusively from unlabeled data, semi-supervised learning methods use both labeled and unlabeled data.

The goal of semi-supervised learning is to improve the accuracy of predictions by using the patterns in unlabeled data.

In contrast to supervised learning, where all data is labeled, semi-supervised learning is useful when it is difficult or expensive to collect a large amount of labeled data. It is also useful when extracting relevant features from data manually is a challenge.

Unsupervised Learning vs. Reinforcement Learning

Unsupervised Learning and Reinforcement Learning (reinforcement learning) differ in the way they deal with labeled and unlabeled data.

Unlike Unsupervised Learning, in Reinforcement Learning algorithms learn by interacting with their environment. The goal is to find an optimal strategy to perform a given task. To do this, reinforcement learning uses a reward system to train the algorithm. That is, for every correct action the algorithm receives a reward and for every incorrect action it receives a punishment. Reinforcement learning is mainly used in robotics, game theory, Automation and others.

unsupervised learning methods

Unsupervised Learning Methods

Depending on the context of the requirements, companies rely on a different Unsupervised Learning method. The following 3 techniques are particularly common:

Cluster analysis

Organizations use cluster analysis to identify natural groupings of data points in a data set. This is done based on similarities or patterns between data points. The idea is to group data points that are similar in some way into the same cluster, while data points with little similarity end up in different clusters.

Practical example

Imagine a company collects data about the purchasing behavior of its customers, including information about purchases, income levels, and age groups. Using cluster analysis, the company divides customers into different groups based on their common shopping behaviors. For example, these clusters might be called "Price Sensitive Shoppers," "Health Conscious Shoppers," and "Luxury Brand Lovers." The company then develops a targeted marketing strategy for each of these groups. This increases customer satisfaction and sales.

Association rules

Companies often use association rules in transactional data analysis to discover patterns and relationships between different products or variables. The goal is to establish rules that show how different elements are related to each other.

Practical example

A classic example is shopping cart analysis. With this, retailers determine, for example, that customers who buy diapers often also buy chocolate. This could be summarized in an association rule such as "If a customer buys diapers, there is a high probability that they will also buy chocolate." A supermarket uses this insight to optimize the placement of diapers and chocolate in the store to increase sales of both products.

Dimensionality reduction

Dimensionality reduction is a technique for reducing the number of features or dimensions in a data set while retaining important information. A commonly used method for this is principal component analysis (PCA).

Practical example

Suppose an organization has a dataset of images containing thousands of pixels. Each pixel represents a feature and the high dimensionality makes analysis and processing difficult. With PCA, the company analyzes the correlations between pixels and identifies a smaller number of "principal components" that explain the greatest variance in the data. With the reduced representation of the data, the company is now able to visualize the data or improve the performance of machine learning algorithms.

unsupervised learning application areas

Application Areas of Unsupervised Learning

Unsupervised learning is used in countless areas. The following list of possible areas of application is therefore only exemplary and in no way exhaustive. Ultimately, companies can use unsupervised learning wherever large volumes of unlabeled data are generated:

Image segmentation in medicine

In medical image processing, image segmentation is a crucial step. Here, medical images, such as X-rays or MRI scans, are divided into different body regions or organs. Unsupervised learning algorithms analyze these images and identify areas that belong together based on similarities in brightness, texture or other features. This enables physicians to examine specific areas of the image in greater detail, helping them to make diagnoses and plan treatments. For example, doctors can precisely identify tumors, blood vessels or tissue structures and ensure the best possible patient care.

Anomaly detection in cybersecurity

In cybersecurity, anomaly detection is critical to identify potential security breaches early. Organizations use unsupervised learning algorithms to model the normal behavior of computer systems or networks. These models capture how users, programs and devices normally behave, detecting deviations or unusual activity. These deviations can then be applied to Cyber attacks, malware infections or other security threats. By detecting such anomalies early, companies are able to take immediate countermeasures.

Natural language processing

In natural language processing, Unsupervised Learning uses text data to automatically identify topics or clusters of documents. This enables deep analysis of large volumes of text. For example, companies classify incoming invoices into categories or customers based on their content. This automates the filing of invoices as well as the verification of account receipts.

Financial Analysis

In finance, Unsupervised Learning plays an important role in portfolio optimization and identifying trading strategies. By analyzing historical market data, algorithms group financial instruments that exhibit similar price movements. These groupings allow investors to create well-diversified portfolios to minimize risks and maximize returns. In addition, the algorithms used detect patterns in financial data that indicate trading strategies. For example, they detect seasonal trends or correlations between different assets.

Recommendation systems in e-commerce

In e-commerce, unsupervised learning approaches analyze customer behavior and recommend products or services based on individual interests. This is often done by identifying patterns and similarities between the preferences and buying behavior of different customers. For example, music recommendation systems on streaming platforms suggest songs that match a particular user's listening preferences. These personalized recommendations improve the shopping experience and increase customer satisfaction.

Genomics and bioinformatics

In genomics and bioinformatics, unsupervised learning techniques play an important role in the analysis of gene expression data and gene sequences. They help group genes that share similar functions or structures. This allows researchers to identify genes involved in specific biological processes or associated with specific diseases. For example, genes that play a role in cancer development could be grouped into clusters to study their functions and interactions. These findings are crucial for drug development and disease research.

Customer segmentation in marketing research

In marketing research, companies use unsupervised learning to divide customers into different segments or clusters based on their buying behavior, preferences, and demographic information. This allows companies to develop targeted marketing strategies for each segment. For example, retailers group customers who frequently buy sports products into one cluster, while they group customers who prefer fashion items into another cluster. By targeting customers in these segments with tailored offers and promotional messages, companies increase customer satisfaction and sales.

Fraud prevention in banking

Financial institutions use unsupervised learning algorithms to model the normal transactional behavior of their customers. By analyzing transaction data, they detect deviations from this normal behavior. These deviations can indicate fraudulent activity, such as stolen credit card information or unauthorized access to bank accounts. Early detection of such anomalies enables financial institutions to act quickly to identify and combat fraud. This not only protects customers' financial assets, but also strengthens their confidence in the bank.

Quality control in manufacturing

In the manufacturing industry, quality assurance identifies defective products and weeds them out before they reach the market. Unsupervised learning methods analyze patterns in sensor data and production processes to detect deviations from normal patterns. These deviations can indicate quality problems, machine malfunctions, or material defects. Early detection of quality problems enables manufacturers to take quick action to improve product quality and minimize scrap. 

Speech recognition

Automatic speech recognition uses techniques such as Hidden Markov Models (HMMs) to recognize and categorize phonemes (sound units) in spoken language. This is the basis for translating and transcribing spoken language into text.

Companies are using speech recognition systems in a variety of applications, from voice assistants like Siri and Alexa to speech recognition in call centers and dictation programs for medical records.

Unsupervised learning enables robust recognition and interpretation of human speech, which greatly improves communication and interaction between humans and machines.

Are you planning to efficiently automate data evaluation in your company? Then talk to one of our experts now without obligation!

Benefits of Unsupervised Learning

We now already know some use cases of unsupervised learning. To understand the possibilities even better, we take a look at the potential benefits that companies - regardless of their industry - can generate with the technology. 

AdvantageExplanationExample
Pattern recognitionUnsupervised Learning helps to automatically discover patterns and structures in data without the need for prior knowledge or examples. This enables the identification of hidden relationships in data sets.A company analyzes sales data and, using unsupervised learning, discovers patterns in customer buying behavior that were not previously apparent, such as frequent joint purchases of certain products.
Classification of unknown dataUnsupervised Learning places new data points into already identified clusters or groups as new data emerges and needs to be placed into existing categories.An online store automatically sorts new products into categories based on their characteristics and similarities to existing products.
Data reductionThrough dimensionality reduction techniques such as PCA, unsupervised learning reduces the number of features or dimensions in a data set. This simplifies data processing and visualization without losing important information.In medical imaging, Unsupervised Learning reduces the number of features in CT scans to analyze them faster without losing diagnostic information.
AutomationUnsupervised Learning automates analysis processes by independently recognizing patterns and structures in large amounts of data. This saves time and resources in manual data interpretation.A logistics company automatically optimizes routes based on traffic data and delivery patterns, without human intervention.
Anomaly detectionThe method is excellent for detecting deviations or anomalies in data, which is essential in cybersecurity to detect potential security breaches early.A security system detects unusual network activity that indicates a possible cyberattack, even if there are no known attack patterns.
PersonalizationIn applications such as recommendation systems, companies use technology to generate personalized recommendations for users based on their interests and preferences. This improves the user experience and increases customer satisfaction.A streaming service recommends movies and series based on a user's viewing habits to increase the likelihood of satisfaction.
Better decision makingIdentifying patterns and relationships in data through Unsupervised Learning helps make more informed decisions, especially in areas such as business, finance, and healthcare.A financial analyst analyzes market data and makes more informed investment decisions based on unsupervised patterns to optimize a client's portfolio.

Challenges of Unsupervised Learning

To fully exploit the potential of unsupervised learning, companies must thoroughly prepare the use and evaluation of their data. In doing so, they encounter these challenges:

Lack of ground truth data

Unsupervised learning is based on unlabeled data. This means that there is no clear reference data or "ground truth" to evaluate the performance of the model. This makes it difficult to review and evaluate the results. Example: suppose you have financial transaction data and want to detect fraudulent transactions, without first labeling the transactions as "fraudulent" or "non-fraudulent." Without ground truth data that clearly categorizes transactions, it is difficult to develop a model that can distinguish fraudulent from legitimate transactions.

Selection of the right number of clusters

In cluster analysis, choosing the optimal number of clusters is an important challenge. An incorrect number will result in unclear or overly fine clusters. Also, it is possible for the technology to miss important patterns. Example: In customer segmentation, you want to divide customers into groups. But if you choose too many clusters, you will have difficulty interpreting the meaning or differences between the groups.

Initialization of the cluster centers

Unsupervised learning algorithms such as K-Means require the selection of initial positions for cluster centers. The choice of unfavorable initializations leads to the fact that the model gets stuck in local minima. Example: When applying K-Means to geographic data, the incorrect selection of initial positions results in clusters that do not effectively split into different geographic regions.

Scalability

Unsupervised learning on large data sets is usually computationally intensive. Scaling algorithms to handle large data sets is therefore often a technical challenge for companies. So, for example, if a company analyzes social media messages in real time, it must ensure that its Unsupervised Learning algorithm is scalable to handle the ever-increasing amount of data available.

Data quality

Unsupervised learning is prone to noise and outliers in the data. If the data is of poor quality or highly contaminated, this leads to unreliable clusters or models. So, for example, if you use text data for topic clustering and there are many misspellings or unclear text, this will lead to inaccurate or confusing clusters.

Interpretability:

Interpreting the results of unsupervised learning is not always easy. This is because the patterns generated are often abstract and difficult to understand. Companies therefore need the expertise to evaluate the data correctly. Example: An Unsupervised Learning model for product placement can identify patterns in purchasing behavior that are difficult for companies to understand, such as the preference for products based on color patterns on packaging.

Overfitting

Unsupervised learning models are susceptible to Overfitting, especially if companies do not adequately regulate the number of clusters or the complexity of the model. This leads to poor generalization on new data. For example, if you set the number of cluster elements too high, a clustering algorithm tends to consider noisy data points and create too many clusters that are not really there.

Selection of the right algorithm

There is a wide variety of unsupervised learning algorithms. Therefore, choosing the right algorithm for a particular dataset or problem is a complex decision. An incorrect algorithm will lead to suboptimal results. Example: If you are developing a model for image recognition and decide to use a text clustering algorithm, the performance is likely to be poor because the algorithm is not suitable for images.

Loss of information with dimensionality reduction

Dimensionality reduction, such as PCA, runs the risk of losing important information in the data. Therefore, selecting the right dimensions to retain is critical. Example: when PCA is used for dimensionality reduction of genetic data, important genetic markers are lost, resulting in a less informative representation.

Use Unsupervised Learning efficiently with Konfuzio

Konfuzio is a proven expert in the automated Extraction and evaluation of unlabeled data from documents. Companies use the software to collect and analyze their data so that they can make well-founded and sustainable business decisions. To do this, Konfuzio combines artificial intelligence, machine learning and deep learning. In practice, this means that companies are able to train the AI with any document and thus generate real added value from any type of data. You can test Konfuzio free of charge to see the software's comprehensive capabilities for yourself. 

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