Multi-dimensional Information Multimedia Big Data Mining Analysis Relying on Association Rule Mapping Model

• Published in: Arabian journal for science and engineering (2011) Vol. 50; no. 10; pp. 7361 - 7373
• Main Author: He, Pengfei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2025; Springer Nature B.V
• Subjects: Algorithms; Audio data; Big Data; Complexity; Data acquisition; Data analysis; Data mining; Datasets; Engineering; Humanities and Social Sciences; Multidimensional data; multidisciplinary; Multimedia; Noise reduction; Parallel processing; Performance measurement; Principal components analysis; Redundancy; Research Article-Computer Engineering and Computer Science; Science; Signal quality; Wiener filtering; Big data mining; Multi-dimensional; Wiener filter; Modified Apriori algorithm; Principal component analysis (PCA)
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-024-09257-2

Multi-factor information multimedia big data mining analysis is a thorough method for generating valuable insights from large, diversified datasets containing multimedia material. Exploration and interpretation of multi-dimensional data, including text, photos, videos, and audio, are part of this sophisticated analytical process. Conventional multi-dimensional information multimedia big data mining analysis suffers from increased processing complexity and has trouble managing high-dimensional data. With parallel processing and a novel pruning strategy, our proposed modified Apriori algorithm effectively addresses these issues, greatly decreasing computational overhead and enhancing scalability for high-dimensional datasets. The data was collected using modern light and electron microscopy techniques. To enhance both the signal's quality and the network's general efficiency, a Wiener filter was used to pre-process the acquired data for noise reduction. Principal component analysis was used to extract pre-processed data (PCA). Multimedia big data mining uses PCA to minimize high-dimensional data while maintaining important information and reducing redundancy. This allows for more effective analysis and feature extraction for improved insights and resource optimization. The proposed approach was tested in a simulated environment, yielding the following performance metrics: accuracy performance (89%), precision (86%) at the level for mining speed (5.37%), mining time (51.4%), acceleration ratio (16.7%), and recall ratio (40.5%). A comparison analysis demonstrates how well the suggested method resolves complexity of networks and data accessibility concerns.