Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges
This addresses clustering challenges in multimedia big data, but appears incremental as it builds on existing Golay Code techniques.
The paper tackles the challenge of clustering multimedia big data by optimizing metaknowledge representation for 23-bit structured and unstructured data, but no concrete results or numbers are provided.
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