First EAI International Conference on Computer Science and Engineering

Research Article

Hash based Frequent Pattern Mining approach to Text Compression

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  • @INPROCEEDINGS{10.4108/eai.27-2-2017.152268,
        author={C. Oswald and S. Srinidhi and K. Sri Vishnu and T.V. Vishal and B. Sivaselvan},
        title={Hash based Frequent Pattern Mining approach to Text Compression},
        proceedings={First EAI International Conference on Computer Science and Engineering},
        publisher={EAI},
        proceedings_a={COMPSE},
        year={2017},
        month={2},
        keywords={Apriori algorithm Compression Ratio Frequent Pattern Mining Huffman Encoding Lossless Com-pression},
        doi={10.4108/eai.27-2-2017.152268}
    }
    
  • C. Oswald
    S. Srinidhi
    K. Sri Vishnu
    T.V. Vishal
    B. Sivaselvan
    Year: 2017
    Hash based Frequent Pattern Mining approach to Text Compression
    COMPSE
    EAI
    DOI: 10.4108/eai.27-2-2017.152268
C. Oswald1,*, S. Srinidhi, K. Sri Vishnu, T.V. Vishal, B. Sivaselvan
  • 1: Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India
*Contact email: coe13d003@iiitdm.ac.in

Abstract

The paper explores the compression perspective of Data Mining. Huffman Encoding is enhanced through Frequent Pattern Mining, a non-trivial phase in Association Rule Mining(ARM) technique, in the field of Data Mining. The seminal Apriori algorithm has been modified in such a way that optimal number of patterns(sequence of characters) are obtained. These patterns are employed in the Encoding process of our algorithm, instead of single character based code assignment approach of Conventional Huffman Encoding. Our approach is built on an efficient hash based data structure, which minimizes the compression time by employing an efficient and novel technique for finding the frequency of the patterns. Simulation over benchmark corpus clearly shows the efficiency of our proposed work in relation to Conventional Huffman Encoding in terms of compression ratio and time.