Applying Clustering Analysis to Heterogeneous Data Using Similarity Matrix Fusion (SMF)

Abstract

We define a heterogeneous dataset as a set of complex objects, that is, those defined by several data types including structured data, images, free text or time series. We envisage this could be extensible to other data types. There are currently research gaps in how to deal with such complex data. In our previous work, we have proposed an intermediary fusion approach called SMF which produces a pairwise matrix of distances between heterogeneous objects by fusing the distances between the individual data types. More precisely, SMF aggregates partial distances that we compute separately from each data type, taking into consideration uncertainty. Consequently, a single fused distance matrix is produced that can be used to produce a clustering using a standard clustering algorithm. In this paper we extend the practical work by evaluating SMF using the k-means algorithm to cluster heterogeneous data. We used a dataset of prostate cancer patients where objects are described by two basic data types, namely: structured and time-series data. We assess the results of clustering using external validation on multiple possible classifications of our patients. The result shows that the SMF approach can improved the clustering configuration when compared with clustering on an individual data type.

Funded by: King Abdulaziz University, Faculty of Computing and Information Technology, Jeddah, Saudi Arabia.