Data Clustering and Classification Algorithms for Big Data Analysis
Summary
Data clustering and classification have become indispensable for extracting actionable insights from large-scale, heterogeneous datasets characterised by high volume, velocity and variety. Clustering techniques partition data into groups based on similarity measures, enabling the discovery of latent patterns without prior labels. Common approaches include centroid-based methods such as k-means, density-based methods like DBSCAN and hierarchical agglomerative strategies. In parallel, classification algorithms assign predefined labels to data instances and encompass decision trees, support vector machines, ensemble methods and neural networks. The scale and complexity of modern data have driven the adoption of distributed computing frameworks—MapReduce and Apache Spark—that facilitate parallel processing and in-memory computation. To address optimisation challenges in both clustering and classification, researchers have integrated metaheuristic algorithms (for example, particle swarm optimisation and artificial bee colony), enhancing solution quality and avoiding local optima. Evaluation of these methods hinges on metrics such as cluster compactness and separation, classification accuracy, as well as computational measures including speedup and scaleup. Across domains from genomics and finance to social media analytics, scalable clustering and classification underpin real-time recommendation, anomaly detection and predictive modelling, highlighting their global significance and practical applications.
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Data Clustering and Classification Algorithms for Big Data Analysis publication trend
The graph below shows the total number of articles in data clustering and classification algorithms for big data analysis across all publications each year (not limited to Nature Index journals).
Technical terms
Big data: Extremely large and complex data sets requiring advanced processing methods.
Clustering: Unsupervised grouping of data points based on similarity measures.
Classification: Supervised assignment of labels to data instances based on learned patterns.
Metaheuristic algorithm: High-level procedures, such as particle swarm optimisation and artificial bee colony, designed for global search in optimisation problems.
MapReduce: Programming model for parallel processing of large data sets across distributed clusters.
Apache Spark: Unified analytics engine for large-scale data processing with in-memory computation.
Variance Ratio Criterion (VRC): Metric for evaluating cluster compactness and separation.
Speedup: Ratio of execution time on a single processor to that on multiple processors.
Scaleup: Measurement of performance when both data size and resources increase proportionally.
References
- CPSO: Chaotic Particle Swarm Optimization for Cluster Analysis. Journal of Artificial Intelligence and Technology (2023).
- Parallelization of the Bison Algorithm Applied to Data Classification. Algorithms (2024).
- A Spark-Based Artificial Bee Colony Algorithm for Unbalanced Large Data Classification. Information (2022).
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