More Insight from Being More Focused: Analysis of Clustered Market Apps

TL;DR

This study demonstrates that clustering mobile apps into homogeneous groups enhances analytical insights, showing that focused samples yield more meaningful correlations than broad, heterogeneous datasets.

Contribution

The paper introduces a clustering-based approach to analyze mobile apps, revealing that homogeneous clusters provide deeper insights than analyzing all apps collectively.

Findings

Clustering apps improves insight compared to whole dataset analysis.

Topic-based similarity does not significantly enhance clustering results.

Homogeneous app clusters lead to more meaningful attribute correlations.

Abstract

The increasing attraction of mobile apps has inspired researchers to analyze apps from different perspectives. As with any software product, apps have different attributes such as size, content maturity, rating, category, or number of downloads. Current research studies mostly consider sampling across all apps. This often results in comparisons of apps being quite different in nature and category (games compared with weather and calendar apps), also being different in size and complexity. Similar to proprietary software and web-based services, more specific results can be expected from looking at more homogeneous samples as they can be received as a result of applying clustering. In this paper, we target homogeneous samples of apps to increase the degree of insight gained from analytics. As a proof-of-concept, we applied the clustering technique DBSCAN and subsequent correlation analysis between app attributes for a set of 940 open-source mobile apps from F-Droid. We showed that (i) clusters of apps with similar characteristics provided more insight compared to applying the same to the whole data and (ii) defining the similarity of apps based on the similarity of topics as created from the topic modeling technique Latent Dirichlet Allocation does not significantly improve clustering results.