Ternary Tree and Clustering Based Huffman Coding Algorithm

In this study, the focus was on the use of ternary tree over binary tree. Here, a new two pass Algorithm for encoding Huffman ternary tree codes was implemented. In this algorithm we tried to find out the codeword length of the symbol. Here I used the concept of Huffman encoding. Huffman encoding was a two pass problem. Here the first pass was to collect the letter frequencies. You need to use that information to create the Huffman tree. Note that char values range from -128 to 127, so you will need to cast them. I stored the data as unsigned chars to solve this problem, and then the range is 0 to 255. Open the output file and write the frequency table to it. Open the input file, read characters from it, gets the codes, and writes the encoding into the output file. Once a Huffman code has been generated, data may be encoded simply by replacing each symbol with its code. To reduce the memory size and fasten the process of finding the codeword length for a symbol in a Huffman tree, we proposed a memory efficient data structure to represent the codeword length of Huffman ternary tree. In this algorithm we tried to find out the length of the code of the symbols used in the tree.