Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges

TL;DR

This paper introduces a novel metaknowledge-based processing technique utilizing Golay Code for clustering multimedia big data, focusing on optimizing metaknowledge representation to improve relational pattern detection.

Contribution

It presents a new approach to extract and optimize metaknowledge from multimedia datasets for improved clustering using Golay Code algorithms.

Findings

Optimized 23-bit metaknowledge representation for multimedia data

Enhanced relational pattern detection in structured and unstructured data

Improved clustering accuracy with the proposed technique

Abstract

Past research has challenged us with the task of showing relational patterns between text-based data and then clustering for predictive analysis using Golay Code technique. We focus on a novel approach to extract metaknowledge in multimedia datasets. Our collaboration has been an on-going task of studying the relational patterns between datapoints based on metafeatures extracted from metaknowledge in multimedia datasets. Those selected are significant to suit the mining technique we applied, Golay Code algorithm. In this research paper we summarize findings in optimization of metaknowledge representation for 23-bit representation of structured and unstructured multimedia data in order to