MACHINE LEARNING

Clustering

Machine Learning

Definition

Clustering is an unsupervised ML technique that automatically groups similar data together. It is used to segment customers based on purchasing behavior, group similar products, identify patterns in support calls. The model doesn't need to be told how many groups to find (in many algorithms): it discovers them autonomously in the data.

Related terms

Machine Learning

K-Means

K-Means is the most widely used clustering algorithm, which divides data into K groups by minimizing the distance between each point and its group center. It is simple, fast, and intuitive. It is used to segment customers, group products by similar characteristics, analyze geographic sales zones, and optimize logistics routes.

Machine Learning

Unsupervised Learning

Unsupervised learning is the ML technique in which the model finds patterns in data without predefined labels. The model autonomously discovers hidden structures such as groups of similar customers (clustering) or data anomalies. It is useful when you don't know what to look for: AI can reveal market segments or anomalous behaviors you hadn't considered.

Machine Learning

Anomaly Detection

Anomaly detection is the ability to automatically identify data or behaviors that deviate significantly from the norm. In business, it is used to detect payment fraud, impending machinery failures (predictive maintenance), anomalies in accounting data, and suspicious behavior in cybersecurity. It transforms data into an early warning system.

EXPLORE

More terms in Machine Learning

Supervised Learning

Supervised learning is the ML technique in which the model learns from labeled examples: for each input, the correct output is provided. For example, by showing thousands of emails labeled as 'spam' or 'not spam', the model learns to classify new emails. It is the most common approach in business applications because it produces reliable and interpretable results.

Unsupervised Learning

Reinforcement Learning

Reinforcement learning is an ML technique in which an agent learns by interacting with an environment and receiving rewards or penalties. It is used to optimize complex strategies such as dynamic pricing, logistics routing, and warehouse management. The agent experiments with different actions and learns which strategy maximizes long-term results.

Classification

Classification is the ML task that assigns a categorical label to an input. Examples: classifying emails (spam/not spam), support tickets (urgent/normal/low), documents (invoice/order/contract), or product images (compliant/defective). It is one of the most widespread and immediate uses of AI in SMEs, with fast and measurable ROI.

Regression

Regression is the ML task that predicts a continuous numerical value. Unlike classification which assigns categories, regression estimates quantities: sales forecasting, real estate price estimation, production cost forecasting. It is fundamental for demand forecasting and financial planning in SMEs.

Random Forest

Random Forest is an ML algorithm that combines hundreds of decision trees to produce robust and accurate predictions. Each tree votes and the final result is the majority's choice. It is popular in business because it works well with minimal tuning, handles missing data, and provides information on which variables are most important for the prediction.

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