In an era where data has become one of the most valuable assets for organizations, it is essential to explore and understand the techniques that allow us to extract meaningful information from massive volumes of data.
Miguel Houghton, Data Scientist at Mercedes-Benz AG, gave an open class on the use of clustering algorithms, principal component analysis (PCA), and anomaly detection to reveal valuable information in large, unlabeled datasets.
We will discover how these techniques allow us to identify patterns, segment customers, and optimize business processes, boosting decision-making and efficiency in the world of Big Data.
First of all, it is important to know that unsupervised learning refers to a branch of machine learning in which the model is trained without the need for explicit labels or guides; that is, the model can discover patterns autonomously in the data.
In Big Data, unsupervised learning is used to explore and understand complex, unstructured information from large datasets. Let’s learn the basics. Let’s get started!
BIG DATA CLUSTERING: DATA GROUPING
It consists of algorithms that allow us to organize the set of elements reflected in the data into groups. Its use begins with the characteristics that define each element and a similarity/distance function that determines their similarities.
Some techniques used are: hierarchical, partitioning, fuzzy, generative or probabilistic clustering algorithms, graph-based algorithms, self-organizing maps, etc.
Among the most important ones are:
- K-Means: This is one of the best-known and most widely used clustering algorithms. Its goal is to divide the data into “K” clusters, where K is a predefined value. It works by iteratively assigning data points to the nearest cluster and recalculating the centroids of each cluster.
- DBSCAN: This is a density-based clustering algorithm capable of identifying arbitrary clusters. It does not require the number of clusters to be specified beforehand and can identify data points as noise if they do not belong to any cluster.
- Hierarchical Clustering: This approach creates a hierarchy of clusters, organizing them into a cluster tree. It can be agglomerative(starting with individual clusters and merging them)or divisive(repeatedly dividing large clusters into smaller subclusters).
Among the business applications of clustering, we find:
- Customer segmentation: Companies can use clustering algorithms to divide their customer base into homogeneous groups based on purchasing behavior, preferences, or demographic characteristics.
- Fraud detection: Algorithms can group similar transactions and highlight those that are atypical compared to a customer’s normal behavior.
- Supply chain optimization: By grouping products with similar demands and characteristics, they can make more efficient decisions about inventory management and distribution routes.
PRINCIPAL COMPONENT ANALYSIS (PCA)
These are mathematical techniques that allow a set of characteristics that define an element to be reduced to a much smaller and more manageable number, avoiding possible distortions (“Curse of dimensionality”).
For its use, projections of the original data matrix are made using algebraic techniques in order to reduce dimensionality while maintaining variability, etc. Among the most used methods are: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Linear Discriminant Analysis (LDA).
Among the business applications that PCA has, we find:
- Dimensionality reduction in data analysis: By transforming high-dimensional data into a smaller set of principal components, companies can retain most of the data variability while reducing complexity.
- Image and multimedia compression: By applying PCA to image representations, it is possible to reduce the amount of information needed to store or transmit an image without a significant loss of visual quality.
- Biological data analysis in the pharmaceutical industry: In the pharmaceutical industry, PCA is used to analyze complex biological data, such as genetic or gene expression profiles. It helps identify patterns and relationships in biomolecular data that can be crucial for drug discovery.
ANOMALY DETECTION
Anomaly detection consists of detecting those elements in a set whose characteristics are significantly different from the rest of the elements.
It is used through different techniques by which each element is associated with a rarity or specificity value that will be used to qualify it as an anomaly or not (outlier), with techniques such as clustering algorithms (Single-Link), jackknife, Local Outlier Factor (LOF) Algorithm, and methods based on nearest neighbors.
When applying anomaly detection to the company, we find:
- Financial transaction fraud detection: This approach looks for unusual patterns in financial transaction behavior and automatically alerts about suspicious activities, such as unauthorized use of credit cards or online fraud.
- Predictive maintenance in industry: Using sensors and operating data, machine conditions are constantly monitored and unusual deviations that could indicate an impending problem are detected.
- Network security and intrusion detection: The primary goal is to identify suspicious behavior or intrusions into computer systems and corporate networks. Anomaly detection algorithms can analyze network traffic for unusual patterns, such as hacking activity, malware, or unauthorized access attempts, and trigger alerts for immediate response.
ASSOCIATION RULES
It involves discovering associations between elements and variables in large datasets. This is achieved by searching for frequent patterns in the data, primarily correlations between specific values of the variables within the dataset. Some of the techniques used include: A priori Algorithm, Eclat Algorithm, and Magnus Opus Algorithm.
Some business examples of the use of association rules:
- Product recommendations in e-commerce: Association rules are used in e-commerce to generate personalized product recommendations. They analyze customers’ past purchase patterns and establish associations between products that are frequently bought together.
- Inventory and supply chain management: Association rules are applied to optimize the selection and placement of products in warehouses and on shelves. By identifying purchasing patterns and relationships between products, companies can make informed decisions about inventory placement, reducing storage costs and improving logistics efficiency.
- Customer data analysis and segmentation: Customer data analysis is used to identify purchasing behavior patterns and preferences. This allows companies to segment customers into groups with similar interests and design targeted marketing strategies for each segment.
SUCCESS STORIES WITH UNSUPERVISED LEARNING
AMAZON
Amazon has the best recommendation system known in a product company. The algorithm is based on the principle that if a customer has purchased products A and B, they are likely also interested in products C and D, which other customers with similar purchasing patterns have purchased.
Customer retention is a success thanks to the recommendation engine and improves as the number of retained customers increases. Around 35% of sales come from personalized recommendations. This recommendation algorithm is enhanced by new data sources such as Alexa, Prime Video, and Prime Music.
Among the numerous unsupervised learning systems implemented by Google, there is a well-known use case based on spam detection in email.
By applying clustering techniques and text analysis, Google identifies spam patterns and automatically separates them from legitimate messages in Gmail. This approach has led to a significant decrease in spam in users’ inboxes and has improved the security and reliability of Google’s email services.
UBER
It is a leader in the passenger transportation industry thanks to its technological power. Uber uses unsupervised learning systems to manage travel demand efficiently.
Uber’s algorithms group drivers in high-demand geographic areas at specific times. This optimizes driver-to-customer matching and reduces wait times.
These algorithms improve the customer experience and lead to positive customer retention. This, combined with data sources like Uber Eats and Lime, allows Uber’s AI systems to improve over time.
CONCLUSIONS
Unsupervised learning has become one of the most significant advances in the business world. Its ability to uncover hidden patterns and segment customers has revolutionized decision-making and business strategy.
These techniques have a cross-cutting impact across a wide range of sectors, from e-commerce to healthcare and manufacturing. As Big Data continues to grow exponentially, these techniques become even more critical, making it essential to fully leverage unlabeled data to understand the nuances and trends in a data-driven world.
If you too want to gain a comprehensive overview of data analytics by learning about data collection, storage, processing, analysis and visualization, as well as the Big Data infrastructure necessary for all of this, the Official Master’s Degree in Management and Analysis of Large Volumes of Data: Big Data is what you are looking for.
This is a 100% online course where you’ll learn modern engineering methods, tools, and techniques, gaining knowledge in a field with excellent career prospects and continuous development. What are you waiting for?
Related Articles:
