Article

A Proposed Incremental Distance Based Clustering of Interesting Patterns for Big Data

Clustering is a fundamental task in unsupervised learning, aiming to group similar data points while maximizing inter-group dissimilarity. Traditional clustering algorithms often struggle with large-scale datasets containing mixed data types (numerical and categorical). This Research introduces an incremental clustering algorithm that incorporates a novelty criterion to identify only interesting patterns from big data. The novelty criterion functions as a distance metric, measuring deviation between instances and labeling an instance as novel if its deviation exceeds a user-defined threshold. Unlike conventional methods such as k-means, the proposed approach does not require predefining the number of clusters; instead, it dynamically determines them during execution. This flexibility enhances its applicability to complex datasets. The algorithm was implemented and evaluated using public datasets, Through experimentation it became clear that the proposed algorithm achieved the same high accuracy of 81.5% as the DBSCAN algorithm. and However it surpassed it in speed, taking only 22 seconds compared to 100 seconds. This means it is more efficient the same level of accuracy but in significantly less time, making it a better option for practical applications.

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Akram A. M. Mustafa
Department of Computer Science, Faculty of Computer Science and Information Technology, Sana’a University, Sana’a, Yemen Department of Computer Science, Faculty of Computer Science and Information Technology, Sana’a University, Sana’a, Yemen
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Abdulmajed A. G. Al-Khulidi
Department of Computer Science, Faculty of Computer Science and Information Technology, Sana’a University, Sana’a, Yemen
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Ahmed S. Al-Hegami
Department of Computer Science, Faculty of Computer Science and Information Technology, Sana’a University, Sana’a, Yemen
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Ahmed S. Al-Hegami, Akram A. M. Mustafa, Abdulmajed A. G. Al-Khulidi, " An Approach for Incremental Parallel Mining of Interesting Clustering Patterns in Big Data", International Journal of Intelligent Systems and Applications in Engineering, 12(4), 4668–4681, 2024, Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7164
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A Proposed Incremental Distance Based Clustering of Interesting Patterns for Big Data. (2026). Sana’a University Journal of Applied Sciences and Technology, 4(4), 1938-1949. https://doi.org/10.59628/jast.v4i4.2271

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