PretopoMD: Pretopology-based Mixed Data Hierarchical Clustering

TL;DR

PretopoMD introduces a pretopology-based hierarchical clustering algorithm for mixed data that avoids dimensionality reduction, using logical rules to improve interpretability and robustness in cluster formation.

Contribution

It presents a novel pretopology approach employing logical rules for hierarchical clustering of mixed data, bypassing traditional dimensionality reduction techniques.

Findings

Demonstrates superior clustering performance on heterogeneous datasets

Provides interpretable clusters directly from raw data

Shows robustness and improved explainability in clustering results

Abstract

This article presents a novel pretopology-based algorithm designed to address the challenges of clustering mixed data without the need for dimensionality reduction. Leveraging Disjunctive Normal Form, our approach formulates customizable logical rules and adjustable hyperparameters that allow for user-defined hierarchical cluster construction and facilitate tailored solutions for heterogeneous datasets. Through hierarchical dendrogram analysis and comparative clustering metrics, our method demonstrates superior performance by accurately and interpretably delineating clusters directly from raw data, thus preserving data integrity. Empirical findings highlight the algorithm's robustness in constructing meaningful clusters and reveal its potential in overcoming issues related to clustered data explainability. The novelty of this work lies in its departure from traditional dimensionality reduction techniques and its innovative use of logical rules that enhance both cluster formation and clarity, thereby contributing a significant advancement to the discourse on clustering mixed data.