preprint · Preprints.org
Analysing smart city data is often complicated by high dimensionality, noise, and overlapping patterns. Standard clustering algorithms like DBSCAN can find arbitrarily shaped clusters and handle noise, but they struggle with ambiguous boundaries and require manual parameter tuning. To address these drawbacks, a hybrid framework integrates fuzzy logic and Particle Swarm Optimisation with DBSCAN. The process standardises data using Z-score normalisation, uses Particle Swarm Optimisation to automate the selection of the core parameters, and incorporates fuzzy logic to support soft clustering for uncertain or overlapping points. Tested on urban analytics datasets from Addis Ababa, this technique achieved better silhouette scores and higher intra-cluster cohesion than standard DBSCAN and its variants, presenting an adaptable method for complex municipal data analysis.
Smart city initiatives rely on finding clear patterns in messy, complex urban data to guide municipal decisions. By automating the tuning of complex algorithms and better managing vague or overlapping information, this approach reduces the need for manual trial and error, making large-scale urban data analysis more dependable and precise.
This methodology could enable software developers and smart city analysts to process noisy municipal data, such as urban mobility records, more reliably. The technology appears to be applied and tested on real-world urban datasets from Addis Ababa, demonstrating readiness for integration into municipal data analysis tools and urban planning platforms.
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The accurate identification of meaningful patterns in high-dimensional and noisy datasets remains a fundamental challenge in intelligent data analysis, particularly within the domain of smart city analytics. Traditional clustering algorithms such as DBSCAN offer robustness to noise and the ability to detect clusters of arbitrary shapes. However, they suffer from critical limitations, including sensitivity to parameter selection and poor performance in handling overlapping or ambiguous data regions. To overcome these issues, this paper presents a novel hybrid clustering framework that synergistically combines fuzzy logic and Particle Swarm Optimization (PSO) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The proposed method begins with Z-score normalization for data standardization, followed by the application of PSO to automatically optimize key DBSCAN parameters, namely Eps and MinPts, across a predefined range. A fuzzy extension of DBSCAN is then employed to enable soft clustering, which better accommodates data uncertainty and overlapping class boundaries. Experimental evaluations on urban analytics datasets from Addis Ababa demonstrate that the proposed approach achieves improved clustering quality, as evidenced by enhanced silhouette scores and intra-cluster cohesion, in comparison to traditional DBSCAN and its variants. This work contributes a flexible and intelligent clustering technique well-suited for real-world smart city applications where data ambiguity and parameter sensitivity are prevalent.
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DOI: 10.20944/preprints202505.1171.v1
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