Leveraging ensemble clustering for privacy-preserving data fusion: Analysis of big social-media data in tourism

Abstract

Discovering knowledge from social media becomes a trend in many domains such as tourism, where users' feedback and rating are the basis of recommendation systems. In this context, cluster analysis has been a major tool to disclose user groups by which the process of collaborative filtering can better determine a personalised suggestion. Matching this to the curse of big data is a challenge with previous studies either implementing conventional techniques on a distributed system or making use of data sampling. Specific to ensemble clustering, only a few aim to obtain both scalability and privacy preserving that are significant to handling social data. This paper presents a new bi-level framework of ensemble clustering in which an instance-segment based analysis is adopted to ensure data privacy and reduce the complexity of clustering the whole dataset. Unlike existing studies, instead of drawing a single clustering from each segment, multiple clusterings are selected to better represent instances therein. Based on published tourism datasets and different experimental settings, the new approach usually outperforms its baselines whilst being competitive to related methods found in the literature. Additional case studies on simulated big datasets and noisy variations are reported and discussed in addition to the analysis of algorithmic parameters.