Bridging the gap between data and understanding for novice users

This paper presents an intuitive tool designed for users who want to explore and understand patterns in their data without needing expertise in complex algorithms. It introduces a novel method that simplifies clustering interpretation by focusing on three critical aspects. It proceeds to create visual, interactive environments that highlight key features of the data that are well suited to assess the quality of a clustering result in many facets. These representations offer clear actionable insights into the data structure. This method seeks to bridge the gap between sophisticated data analysis techniques and non-technical users by providing an intuitive, accessible, easy-to-use platform for exploring and assessing the quality of their clustering results. It extends its utility to text data by encompassing a framework that allows for quality evaluation of clusters resulting from textual datasets by leveraging key analytical markers - centroids, boundary and extreme points - to review their distinctiveness, representativeness and coherence. Our approach offers a practical solution to the cluster assessment and interpretation challenges faced by novice users. It is particularly well-suited for persons aiming to understand various relationships within their clustering results which may be crucial to them for drawing conclusions about human behavior, societal trends among other things.