Background: Nowadays, the mobile app market becomes rapidly increased in world wide. The mobile app marketers have smart enough to understand the requirements and demands of customers and perform their aspirations. They delight them. It provides growth, profitability, and creativity with lot of inventions. The main aim of this research is to analyze the customer interest and preferences of mobile service providers.
Methodology: This paper proposed the clustering model named as Hierarchical Flexi-Ensemble Clustering (HFEC). It provides the final result with robustness and improved quality. Before clustering, the unwanted features are removed by using the Genetic Algorithm based on the Collective Materials (GACM) technique. The customer preferences are analyzes with the clustering of mobile usage patterns.
Results: The analysis determined that the app usage pattern based on the most frequent word, rating category, rating character count, rating word count and content-based rating in the google play store app dataset. Finally, the results are compared with the existing methods to analyze the superior performance of proposed method. The comparison analysis is estimated based on the based on the average hit rate at different cache sizes.
Conclusion: The work is concluded with the app pattern prediction in the form of clustering for app marketing service. From the marketing side, they can analyze the customer preferences and satisfaction.