Visual analytics in FCA-based clustering
This work addresses the issue of cluster interpretability for analysts in social network analysis and recommender systems, but it appears incremental as it focuses on a testing framework rather than a new algorithmic breakthrough.
The paper tackles the problem of triclustering algorithms in Formal Concept Analysis (FCA) not always producing meaningful clusters, by developing a visual analytics platform prototype to help analysts evaluate and make decisions on triclusters and recommendations.
Visual analytics is a subdomain of data analysis which combines both human and machine analytical abilities and is applied mostly in decision-making and data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was developed to detect groups of objects with similar properties under similar conditions. It is used in Social Network Analysis (SNA) and is a basis for certain types of recommender systems. The problem of triclustering algorithms is that they do not always produce meaningful clusters. This article describes a specific triclustering algorithm and a prototype of a visual analytics platform for working with obtained clusters. This tool is designed as a testing frameworkis and is intended to help an analyst to grasp the results of triclustering and recommender algorithms, and to make decisions on meaningfulness of certain triclusters and recommendations.