Details

Autor(en) / Beteiligte

• Calantoc, Denver Jhon R.
• Dasig, Daniel D.
• Claricia, Eleonora E.
• Taduyo, Mary Ann B.
• Gatpandan, Paulino H.
• Guarin, Rudolph Val F.

Titel

Clustering and Predicting Smartphones Features using Gaussian Mixture Model Algorithm

Ist Teil von

• 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2021, p.1-6

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE/IET Electronic Library

Beschreibungen/Notizen

• The Gaussian Mixture Model algorithm was used to identify common functionality and patterns from the Smartphone dataset, and address the overlap of clustered data in other clustering algorithms. Python, a multi-paradigm programming language, and Knowledge Discovery in Databases (KDD) method were utilized. As gleaned from the results, smartphones with a higher camera, high memory, higher battery, and a bigger display size could be considered as camera smartphones. Moreover, smartphones with high resolution, bigger screens, higher memory, and battery storage but of lower cost could be considered as gaming and entertainment smartphones. Two models were developed using Multiple Regression to predict smartphone display size and battery capacity. Gaussian Mixture Model algorithm addressed overlap of clustered data minimally, as such; there are much cheaper smartphones in the market that have common functionality on popular brands. Further empirical studies could be conducted to provide insightful recommendations to the customers and the smartphones companies.