An optimal text compression algorithm based on frequent pattern mining
Data Compression as a research area has been explored in depth over the years resulting in Huffman Encoding, LZ77, LZW, GZip, RAR, etc. Much of the research has been focused on conventional character/word based mechanism without looking at the larger perspective of pattern retrieval from dense and l...
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| Published in: | Journal of ambient intelligence and humanized computing Vol. 9; no. 3; pp. 803 - 822 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2018
Springer Nature B.V |
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| ISSN: | 1868-5137, 1868-5145 |
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| Abstract | Data Compression as a research area has been explored in depth over the years resulting in Huffman Encoding, LZ77, LZW, GZip, RAR, etc. Much of the research has been focused on conventional character/word based mechanism without looking at the larger perspective of pattern retrieval from dense and large datasets. We explore the compression perspective of Data Mining suggested by Naren Ramakrishnan et al. where in Huffman Encoding is enhanced through frequent pattern mining (FPM) a non-trivial phase in Association Rule Mining (ARM) technique. The paper proposes a novel frequent pattern mining based Huffman Encoding algorithm for Text data and employs a Hash table in the process of Frequent Pattern counting. The proposed algorithm operates on pruned set of frequent patterns and also is efficient in terms of database scan and storage space by reducing the code table size. Optimal (pruned) set of patterns is employed in the encoding process instead of character based approach of Conventional Huffman. Simulation results over 18 benchmark corpora demonstrate the betterment in compression ratio ranging from 18.49% over sparse datasets to 751% over dense datasets. It is also demonstrated that the proposed algorithm achieves pattern space reduction ranging from 5% over sparse datasets to 502% in dense corpus. |
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| AbstractList | Data Compression as a research area has been explored in depth over the years resulting in Huffman Encoding, LZ77, LZW, GZip, RAR, etc. Much of the research has been focused on conventional character/word based mechanism without looking at the larger perspective of pattern retrieval from dense and large datasets. We explore the compression perspective of Data Mining suggested by Naren Ramakrishnan et al. where in Huffman Encoding is enhanced through frequent pattern mining (FPM) a non-trivial phase in Association Rule Mining (ARM) technique. The paper proposes a novel frequent pattern mining based Huffman Encoding algorithm for Text data and employs a Hash table in the process of Frequent Pattern counting. The proposed algorithm operates on pruned set of frequent patterns and also is efficient in terms of database scan and storage space by reducing the code table size. Optimal (pruned) set of patterns is employed in the encoding process instead of character based approach of Conventional Huffman. Simulation results over 18 benchmark corpora demonstrate the betterment in compression ratio ranging from 18.49% over sparse datasets to 751% over dense datasets. It is also demonstrated that the proposed algorithm achieves pattern space reduction ranging from 5% over sparse datasets to 502% in dense corpus. |
| Author | Oswald, C. Sivaselvan, B. |
| Author_xml | – sequence: 1 givenname: C. orcidid: 0000-0002-1251-1495 surname: Oswald fullname: Oswald, C. email: oswald.mecse@gmail.com organization: Department of Computer Engineering, Indian Institute of Information Technology, Design and Manufacturing Kancheepuram – sequence: 2 givenname: B. surname: Sivaselvan fullname: Sivaselvan, B. organization: Department of Computer Engineering, Indian Institute of Information Technology, Design and Manufacturing Kancheepuram |
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| Keywords | Apriori algorithm Frequent pattern mining Lossless compression Huffman encoding Compression ratio |
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| References_xml | – reference: BrinSMotwaniRUllmanJDTsurSDynamic itemset counting and implication rules for market basket dataACM SIGMOD Record ACM19972625526410.1145/253262.253325 – reference: (1989) Calgary and canterbury compression corpus datasets. http://corpus.canterbury.ac.nz/descriptions/. Accessed 12 Jan 2017 – reference: Williams RN (1991) An extremely fast ziv-lempel data compression algorithm. In: Data Compression Conference, 1991. DCC’91., IEEE, pp 362–371 – reference: Park JS, Chen Ms, Yu PS (1995) An effective hash-based algorithm for mining association rules, vol 24. ACM – reference: Storer JA (1988) Data compression: methods and theory. Computer Science Press, Inc – reference: (1987) UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/Census+Income. Accessed 14 Jan 2017 – reference: WittenIHNealRMClearyJGArithmetic coding for data compressionCommun ACM198730652054010.1145/214762.214771 – reference: Feigenblat G, Porat E, Shiftan A (2016) Linear time succinct indexable dictionary construction with applications. In: Data Compression Conference (DCC), 2016, IEEE, pp 13–22 – reference: RamakrishnanNGramaAData mining: From serendipity to science—guest editors’ introductionIEEE Comput1999328343710.1109/2.781632 – reference: LabeitJShunJBlellochGEParallel lightweight wavelet tree, suffix array and fm-index constructionJ Disc Algorithms201743217363895710.1016/j.jda.2017.04.00106723733 – reference: ShannonCEA mathematical theory of communicationACM SIGMOBILE Mobile Comput Commun Rev20015135555211910.1145/584091.584093 – reference: ZivJLempelAA universal algorithm for sequential data compressionIEEE Trans Inf Theory197723333734353021510.1109/TIT.1977.10557140379.94010 – reference: Kempa D, Kosolobov D (2017) Lz-end parsing in compressed space. 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