Novel Metaknowledge-Based Processing Technique for Multimediata Big Data Clustering Challenges

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 be processed in 23-bit Golay Code for cluster recognition.

Construction of FuzzyFind Dictionary using Golay Coding Transformation for Searching Applications

“…43 [2] 108 [3] 199 [4] 1920 [5] 3840 [6] 176236 [7] 176327 [8] 178048 [9] 179968 [10] 442567 [11] 444288 [12] 446208 [13] 819072 [14] 816896 [15] 7868160 1000  [0] 5244416 [1] 202860 [2] 202947 [3] 203166 [4] 204288 [5] 202496 [6] 442563 [7] 442782 [8] 443904 [9] 446208 [10] 799134 [11] 800256 [12] 802560 [13] 1697280 [14] 1699584 [15] 6295296 1000  00000000000001111101000 480  00000000000000111100000 Figure 5 : Sample result of FuzzyFind Dictionary and shows that two indices with hamming distance of less or equal than two has at least one same mapping index.…”

“…This method is an implementation of Golay transformation in conjunction with 23 bits and allows for error correction with two hashes utilizing the overlapping hash values [14]. In previous experiments, we concluded using the hash table is the most efficient method way for accessing data in constant time [13], Since 2014 we have been able to use the GCTHT in constructing a 23-bit meta-knowledge template for Big Data Discovery allowing for meta-feature extraction for clustering Structured and Unstructured Data (text-based and multimedia) [1,2]. Yet the traditional use of GCTHT was hampering the ability to use fuzzy logic and we also realized the order of extracted metafeatures was critical for Big Data clusterization.…”

“…Golay Coding clustering technique which has faster time complexity in comparison with previous conventional methods such as K-means, spectral clustering and hierarchical clustering [8,11] . Also the traditional method 1 National Aeronautics and Space Administration (NASA) of clustering cannot cover fuzzy logic. This method is used for error correction, clustering, and other aspect in computer science.…”

FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

“…BGC utilizes 24 bits, however, a perfected version of the Golay Code algorithm works in a linear time complexity using 23 bits [16], [17]. The algorithm used and implemented in this research study was inspired by the Golay Code clustering hash table [18], [19], [17], [20]. This research offers two main differences and improvements: i) it works with n features whereas Golay code has a limitation of 23 bits ii) our method utilizes supervised learning while Golay Code is an unsupervised algorithm which basically is a Fuzzy Clustering method.…”

“…The Golay code generate hash table with six indices for labelling Binary Features (BF) as fuzziness labeled but FSL-BM is supervised learning is induced techniques of encoding and decoding into two labels or sometimes fuzzy logics classifiers by using probability or similarity. Between 2014 and 2015, the several studies addressed on using the Golay Code Transformation Hash table (GCTHT) in constructing a 23-bit meta-knowledge template for Big Data Discovery which allows for meta-feature extraction for clustering Structured and Unstructured Data (text-based and multimedia) [21], [19].…”