New Clustering Method Simplifies Analysis of Large Data Sets
Researchers from HSE University and the Institute of Control Sciences of the Russian Academy of Sciences have proposed a new method of data analysis: tunnel clustering. It allows for the rapid identification of groups of similar objects and requires fewer computational resources than traditional methods. Depending on the data configuration, the algorithm can operate dozens of times faster than its counterparts. The study was published in the journal Doklady Rossijskoj Akademii Nauk. Mathematika, Informatika, Processy Upravlenia.
Each year, the volume of information requiring processing continues to grow. Data comes from a variety of sources: scientific research, financial reports, medical examinations, and many others. Clustering methods—which group data based on similar characteristics—are used to detect patterns and organise information within such large datasets. These groupings are known as clusters.
One of the most widely used clustering methods is the k-means algorithm. It divides data into a predetermined number of clusters, initially selecting their centres (centroids). However, this method has a limitation: the number of clusters must be known beforehand, which is not always possible when dealing with complex data. Scientists from HSE University and the V.A. Trapeznikov Institute of Control Sciences have proposed a new approach to simplify this process—tunnel clustering. Unlike the k-means method, this algorithm does not require the number of clusters to be set in advance; it determines the necessary number itself by analysing the data structure.
‘The algorithm forms “tunnels” in the data—regions in multidimensional space where objects with similar characteristics group together,’ explained Fuad Aleskerov, Head of the Department of Mathematics at the HSE Faculty of Economic Sciences. ‘Users can choose from three modes of operation: with fixed cluster boundaries, with adaptive boundaries that adjust to the data structure, or a combined approach. This makes the method flexible and suitable for various types of tasks.’
The method was tested on a synthetic (artificially generated) dataset of 100,000 objects, as well as on real-world tasks in public administration and the banking sector.
The main advantage of the new method is its speed. Unlike classical algorithms that demand significant computational resources, tunnel clustering can, depending on the data configuration, perform the analysis dozens of times faster.
In addition, the researchers introduced the concept of the ‘transition degree’—a parameter indicating how many characteristics of an object must change for it to be classified into a different cluster. This helps assess the clarity of cluster boundaries and identify objects situated at the intersection of different groups.
‘People are generating more and more data, and the pace is only accelerating. According to the latest Digital 2025: Global Overview Report, as of early 2025, there were 5.56 billion internet users—nearly 68% of the global population. Adults spend an average of 6 hours and 38 minutes online each day, communicating, working, watching videos, and consuming content,’ said Alexey Myachin, Senior Research Fellow at the HSE International Centre for Decision Choice and Analysis. ‘Companies that ignore data analysis are losing vast sums of money.’
The authors continue to refine the algorithm, including conducting research into dimensionality reduction, which will help further decrease the time required to identify patterns in data.
The study was carried out with partial support from the Russian Science Foundation.
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