Comparison of Compression Algorithms in text data for Data Mining

Abstract

Text data compression is a process of reducing text size by encrypting it. In our daily lives, we sometimes come to the point where the physical limitations of a text or data sent to work with it need to be addressed more quickly. Ways to create text compression should be adaptive and challenging. Compressing text data without losing the original context is important because it significantly reduces storage and communication costs. In this study, the focus is on different techniques of encoding text files and comparing them in data processing. Some efficient memory encryption programs are analyzed and executed in this work. These include: Shannon Fano coding, Hoffman coding, Hoffman comparative coding, LZW coding and Run-length coding. These analyzes show how these coding techniques work, how much compression for these coding techniques. Writing, the amount of memory required for each technique, a comparison between these techniques is possible to find out which technique is better in which conditions. Experiments have shown that Hoffman's comparative coding shows a higher compression ratio. In addition, the improved RLE encoding suggests a higher performance than the typical example in compressing data in text.