Progress in Signals and Telecommunication Engineering Hsing-Cheng Liu, Yao-Liang Chung 15 1 2026 https://doi.org/10.6025/pste/2026/15/1/1-13 https://www.dline.info/pste/fulltext/v15n1/pstev15n1_1.pdf 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.