Visual analytics in FCA-based clustering

TL;DR

This paper presents a visual analytics platform for FCA-based triclustering, aiding analysts in interpreting clusters and recommendations in social network analysis and decision-making tasks.

Contribution

It introduces a specific triclustering algorithm and a visual analytics prototype to improve understanding and evaluation of clusters.

Findings

Prototype helps analysts interpret triclusters

Platform improves decision-making in social network analysis

Assists in assessing meaningfulness of clusters

Abstract

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.