Multi-Label Categorical Data using Orthogonal-Constrained Meta-Heuristic Adaptive Multi-View Clustering

Abstract

Clustering is a fundamental concept in data mining for real-time data processing; nevertheless, assessing how well attributes are represented in clustering is a major challenge in AI-related fields. A popular idea in multi-labeled categorical data analysis, multi-labeled clustering provides a wealth of useful information for attribute assessment and representation. The objective of multi-dimensional clustering is to produce accurate clustering results under varied settings by combining complementing data from several dimensions.To visualize the data as a cluster with several categories, we offer a new method in this study called Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering (OCMHAMVC). One method that has been suggested uses multi-labeled data to group comparable labeled samples into dimensional data concepts and then uses the optimal matrix factorization (OMF) method to assess low-dimensional data. Then, we use an adaptive heuristic to merge complimentary data from multiple dimensions and show the data in an orthonormality-constrained way. We also add complexity to the computational analysis of the data. Testing the suggested method on large datasets with many views yields efficient and scalable results when compared to more conventional clustering methods that are connected to various perspectives.