Specification-Driven Segmentation of Mobile Devices: A Cluster Profiling and PCA-Based Visual Analysis
Hsing-Cheng Liu, Yao-Liang Chung
Progress in Signals and Telecommunication Engineering, Vol. 15, No. 1, 2026.
DOI: https://doi.org/10.6025/pste/2026/15/1/1-13
Link: https://www.dline.info/pste/fulltext/v15n1/pstev15n1_1.pdf
Abstract:
The rapid diversification of mobile devices poses significant challenges for market analysis, application
deployment, and system level design optimization. This study presents a specification driven segmentation
framework that integrates cluster profiling with principal component analysis (PCA) based visualization to
characterize heterogeneous mobile device ecosystems. Using a structured Kaggle telecommunications dataset
encompassing hardware specifications, display characteristics, camera features, connectivity options, and
pricing attributes, we apply unsupervised clustering to identify distinct device categories. Z-score
standardization ensures feature comparability, while centroid based profiling captures group-level
specification patterns. Dual visualization strategies are employed: radar plots enable intuitive multivariate
comparison of normalized feature means across clusters, revealing interpretable trade,offs between
performance, physical design, and economic positioning; two dimensional PCA projections validate cluster
separability and expose dominant latent factors governing device differentiation, with explained variance
ratios quantifying the reliability of reduced dimension representations. Results demonstrate clear
segmentation into entry level, mid range, and feature rich device categories, with discriminant features
including RAM, battery capacity, camera resolution, and processing power. Radar plots facilitate rapid
qualitative assessment of cluster personalities, while PCA confirms mathematical distinctness among groups
and identifies performance capability and multimedia features as primary differentiation axes. The framework
offers a scalable, interpretable methodology for analyzing mobile device diversity, supporting data informed
decision making for product positioning, adaptive application deployment, and market intelligence. By
bridging traditional engineering perspectives with modern data science practices, this approach contributes
to more responsive, user centric innovation in mobile and wireless system design, with implications for
digital engineering and context aware mobile computing applications.