Improving Golay Code Using Hashing Technique

Abstract

Data are the representation of our world and our life. Data are increasing continuously, they come from different sources such as sensors, maps, climate informatics, smartphones, social media and/or medical data domains. Data are represented by different forms such as image, text, video and/or digital data. These incomprehensible data need an influential technique to be clustered and analyzed. This paper presents a hashing technique for the clustering process of unclassified and disorganized data. These clustered data are useful for decision-making process. The proposed technique is based on Golay error-correction code. The main concept is reversing the original Golay error-correction scheme and building Golay Code Addresses Hash Table (GCAHT). Simulation results stated that the proposed technique achieved high performance. Beta-CV, Dunn Index, C-index and Sum Square Error are used for measurements.