Data clustering: application and trends.

Area of Science:

• Data Science
• Machine Learning
• Artificial Intelligence

Background:

• Clustering is a key analytical technique for grouping unlabeled data to extract meaningful information.
• The diversity of clustering problems necessitates a variety of algorithms, each with specific applications.
• This review focuses on recent advancements and applications of data clustering.

Purpose of the Study:

• To review data clustering techniques and their applications.
• To highlight recent industrial applications and emerging concepts in clustering.
• To underscore the continued relevance of clustering for researchers and industry.

Main Methods:

• Discussion of fundamental clustering components and classification terminologies.
• Outline of various clustering algorithms, their variants, and similarity/dissimilarity measures.
• Examination of challenges in clustering optimization, validation, and data types.

Main Results:

• Data size is a critical classification criterion; larger, varied datasets require advanced feature extraction, validation, and clustering techniques for optimal cluster determination.
• Clustering is increasingly integrated with other analytical methods in key industrial sectors.
• Significant applications are observed in manufacturing, transportation, logistics, energy, and healthcare, aligning with sustainable development goals.

Conclusions:

• The growing complexity and size of data necessitate innovative approaches to clustering.
• Clustering techniques are becoming integral components of broader analytical frameworks in industry.
• Continued research and development in clustering are vital for both academic and industrial progress.