Data clustering, or cluster analysis, is the process of grouping data items so that similar items belong to the same group/cluster. There are many clustering techniques. In this article I'll explain ...

Data clustering remains an essential component of unsupervised learning, enabling the exploration and interpretation of complex datasets. The field has witnessed considerable advancements that address ...

Spectral clustering is quite complex, but it can reveal patterns in data that aren't revealed by other clustering techniques. Data clustering is the process of grouping data items so that similar ...

The optimisation of software systems has become increasingly critical in enhancing the maintainability, performance and scalability of complex applications. Recent advances in clustering techniques ...

Clustering is the assignment of data objects (records) into groups (called clusters) so that data objects from the same cluster are more similar to each other than objects from different clusters.

Clustering is a commonly considered data mining problem in the text domains. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, ...

A hybrid method for clustering multivariate observations is proposed, which combines elements of the k-means and the single-linkage clustering techniques. One purpose of the proposed method is to ...

In materials science, substances are often classified based on defining factors such as their elemental composition or crystalline structure. This classification is crucial for advances in materials ...

The Journal of the Operational Research Society, Vol. 59, No. 11 (Nov., 2008), pp. 1532-1546 (15 pages) In this paper, a parallel clustering technique and route construction heuristic have been ...