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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| Veröffentlicht in: | Journal of ambient intelligence and humanized computing Jg. 9; H. 3; S. 803 - 822 |
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| Sprache: | Englisch |
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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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| Cites_doi | 10.1016/j.ins.2014.02.060 10.1109/40.7773 10.1145/1133905.1133914 10.1145/956750.956788 10.1109/DCC.2016.30 10.1023/B:DAMI.0000005258.31418.83 10.1007/BF01936138 10.1007/s10618-006-0059-1 10.1145/322344.322346 10.1109/69.846291 10.1016/j.eswa.2014.01.025 10.1002/widm.1074 10.1145/775047.775081 10.1145/1133905.1133916 10.1145/214762.214771 10.1007/978-3-319-26832-3_53 10.1145/322234.322237 10.1145/5684.5688 10.1109/TIT.1978.1055934 10.1109/TIT.1977.1055714 10.1109/DCC.2016.60 10.1109/2.781632 10.1145/568271.223813 10.1109/DCC.2016.38 10.1016/j.jda.2017.04.001 10.1109/DCC.2016.24 10.1145/380995.381017 10.1109/DCC.2017.35 10.1109/26.61469 10.1109/TIT.1966.1053907 10.1145/253262.253325 10.1016/j.eswa.2015.03.004 10.4108/eai.27-2-2017.152268 10.1145/31846.42227 10.1109/DCC.2017.73 10.1007/978-3-642-82456-2_9 10.1145/584091.584093 10.1109/SPCOM.2016.7746651 10.1109/DCC.2016.70 10.1109/DCC.1991.213344 |
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| Keywords | Apriori algorithm Frequent pattern mining Lossless compression Huffman encoding Compression ratio |
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| References | ToivonenHSampling large databases for association rulesVLDB199696134145 RamakrishnanNGramaAData mining: From serendipity to science—guest editors’ introductionIEEE Comput1999328343710.1109/2.781632 (1989) Calgary and canterbury compression corpus datasets. http://corpus.canterbury.ac.nz/descriptions/. Accessed 12 Jan 2017 Park JS, Chen Ms, Yu PS (1995) An effective hash-based algorithm for mining association rules, vol 24. ACM Pratas D, Pinho AJ, Ferreira PJ (2016) Efficient compression of genomic sequences. In: Data Compression Conference (DCC), 2016, IEEE, pp 231–240 Uno T, Kiyomi M, Arimura H (2005) Lcm ver. 3: Collaboration of array, bitmap and prefix tree for frequent itemset mining. In: Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations, ACM, pp 77–86 Köppl D, Sadakane K (2016) Lempel-ziv computation in compressed space (lz-cics). In: Data Compression Conference (DCC), 2016, IEEE, pp 3–12 Savasere A, Omicinski ER, Navathe SB (1995) An efficient algorithm for mining association rules in large databases Decaroli G, Gagie T, Manzini G (2017) A compact index for order-preserving pattern matching. In: Data Compression Conference (DCC), 2017, IEEE, pp 72–81 Li W, Yao Y (2016) Accelerate data compression in file system. In: Data Compression Conference (DCC), 2016, IEEE, pp 615–615 BorgeltCFrequent item set miningWiley Interdiscip Rev Data Min Knowl Discov20122643745610.1002/widm.1074 Liu J, Pan Y, Wang K, Han J (2002) Mining frequent item sets by opportunistic projection. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 229–238 RamabadranTVGaitondeSSA tutorial on crc computationsIEEE Micro19884627510.1109/40.7773 Bledsoe RE (1987) Data communication with modified Huffman coding Deorowicz S (2003) Universal lossless data compression algorithms. Philosophy Dissertation Thesis, Gliwice (2014) Amazon reviews dataset. http://jmcauley.ucsd.edu/data/amazon/. Accessed 03 June 2017 NelsonMRLzw data compressionDr Dobb’s J198914102936 Oswald C, Ghosh AI, Sivaselvan B (2015) An efficient text compression algorithm-data mining perspective. In: Mining Intelligence and Knowledge Exploration, Springer, pp 563–575 ShannonCEA mathematical theory of communicationACM SIGMOBILE Mobile Comput Commun Rev20015135555211910.1145/584091.584093 LabeitJShunJBlellochGEParallel lightweight wavelet tree, suffix array and fm-index constructionJ Disc Algorithms201743217363895710.1016/j.jda.2017.04.00106723733 Zaki MJ (2000) Scalable algorithms for association mining. IEEE Trans Knowl Data Eng 12(3):372–390. http://dblp.uni-trier.de/db/journals/tkde/tkde12.htmlZaki00 (1987) UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/Census+Income. Accessed 14 Jan 2017 Kempa D, Kosolobov D (2017) Lz-end parsing in compressed space. In: Data Compression Conference (DCC), 2017, IEEE, pp 350–359 Miller VS, Wegman MN (1985) Variations on a theme by ziv and lempel. In: Combinatorial algorithms on words, Springer, pp 131–140 ZivJLempelACompression of individual sequences via variable-rate codingIEEE Trans Inf Theory197824553053650746510.1109/TIT.1978.10559340392.94004 Basu S, Chaturvedi S, Hegde RM (2016) Text compression using lexicographic permutation of binary strings. In: 2016 International Conference on Signal Processing and Communications (SPCOM), IEEE, pp 1–5 RodehMPrattVREvenSLinear algorithm for data compression via string matchingJ ACM (JACM)1981281162460318010.1145/322234.3222370462.94015 Williams RN (1991) An extremely fast ziv-lempel data compression algorithm. In: Data Compression Conference, 1991. DCC’91., IEEE, pp 362–371 (2006) Rar compression algorithm. http://www.rarlab.com/. Accessed 12 Jan 2017 (2008) PAQ. http://www.cs.fit.edu/~mmahoney/compression. Accessed 02 June 2017 Borgelt C (2005) Keeping things simple: finding frequent item sets by recursive elimination. In: Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations, ACM, pp 66–70 OswaldCSrinidhiSVishnuKSVishalTSivaselvanBHash based frequent pattern mining approach to text compressionEAI2017 Burrows M, Wheeler DJ (1994) A block-sorting lossless data compression algorithm Zaki MJ, Gouda K (2003) Fast vertical mining using diffsets. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 326–335 BentleyJLSleatorDDTarjanREWeiVKA locally adaptive data compression schemeCommun ACM198629432033083504410.1145/5684.56880648.94007 BastideYTaouilRPasquierNStummeGLakhalLMining frequent patterns with counting inferenceACM SIGKDD Explor Newslett200022667510.1145/380995.3810170983.68511 Bastide Y, Taouil R, Pasquier N, Stumme G, Lakhal L (2000a) Mining frequent patterns with counting inference. SIGKDD Explor Newsl 2(2):66–75. doi:10.1145/380995.381017 HanJChengHXinDYanXFrequent pattern mining: current status and future directionsData Min Knowl Discov20071515586238077710.1007/s10618-006-0059-1 DengZHLvSLPrepost+: an efficient n-lists-based algorithm for mining frequent itemsets via children–parent equivalence pruningExpert Syst Appl201542135424543210.1016/j.eswa.2015.03.004 (2013) Silesia dataset. http://sun.aei.polsl.pl/~sdeor/index.php?page=silesia. Accessed 12 Jan 2017 Goethals B (2003) Survey on frequent pattern mining, manuscript ZivJLempelAA universal algorithm for sequential data compressionIEEE Trans Inf Theory197723333734353021510.1109/TIT.1977.10557140379.94010 Storer JA (1988) Data compression: methods and theory. Computer Science Press, Inc Pei J, Han J, Lu H, Nishio S, Tang S, Yang D (2001) H-mine: Hyper-structure mining of frequent patterns in large databases. In: Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on, IEEE, pp 441–448 JakobssonMCompression of character strings by an adaptive dictionaryBIT Num Math198525459360310.1007/BF01936138 Agarwal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Bocca JB, Jarke M, Zaniolo C (eds) VLDB’94, Proceedings of 20th International Conference on Very Large Data Bases, September 12–15, 1994. Morgan Kaufmann, Santiago de Chile, Chile, pp 487–499 PountainDRun-length encodingByte1987126317319 VitterJSDesign and analysis of dynamic huffman codesJ ACM (JACM)198734482584591384410.1145/31846.422270637.94002 (2005) TREC genomics track data. http://skynet.ohsu.edu/trec-gen/data/2004/. Accessed 12 Jan 2017 StorerJASzymanskiTGData compression via textual substitutionJ ACM (JACM)198229492895167425210.1145/322344.3223460489.68041 Han J, Kamber M (2000) Data mining: concepts and techniques. Morgan Kaufmann Golomb S (2006) Run-length encodings (corresp.). IEEE Trans Inf Theor 12(3):399–401. doi:10.1109/TIT.1966.1053907 HanJPeiJYinYMaoRMining frequent patterns without candidate generation: a frequent-pattern tree approachData Min Knowl Discov2004815387203783110.1023/B:DAMI.0000005258.31418.83 DavidA H (1952) A method for the construction of minimum redundancy codes. Proc IRE 40(9):1098–1101 (2013) Debruijn sequence data. http://bioinf.spbau.ru/en/spadesmanual. Accessed 03 June 2017 BrinSMotwaniRUllmanJDTsurSDynamic itemset counting and implication rules for market basket dataACM SIGMOD Record ACM19972625526410.1145/253262.253325 DengZHLvSLFast mining frequent itemsets using nodesetsExpert Syst Appld201441104505451210.1016/j.eswa.2014.01.025 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 BrentRPA linear algorithm for data compressionAust Comput J198719264680641.68029 (2003) Deflate compression algorithm. http://www.pkware.com. Accessed 12 Jan 2017 David S (2004) Data compression: the complete reference, 2nd edn. http://www.ecs.csun.edu/~dxs/DC3advertis/Dcomp3Ad.html WittenIHNealRMClearyJGArithmetic coding for data compressionCommun ACM198730652054010.1145/214762.214771 LinKCLiaoIEChangTPLinSFA frequent itemset mining algorithm based on the principle of inclusion–exclusion and transaction mappingInf Sci201427627828910.1016/j.ins.2014.02.060 Policriti A, Prezza N (2016) Computing lz77 in run-compressed space. In: Data Compression Conference (DCC), 2016, IEEE, pp 23–32 MoffatAImplementing the ppm data compression schemeIEEE Trans Commun199038111917192110.1109/26.61469 540_CR37 540_CR39 M Rodeh (540_CR52) 1981; 28 540_CR43 540_CR46 540_CR45 M Jakobsson (540_CR33) 1985; 25 540_CR40 540_CR29 J Han (540_CR32) 2007; 15 540_CR26 540_CR28 540_CR27 C Borgelt (540_CR17) 2012; 2 A Moffat (540_CR41) 1990; 38 Y Bastide (540_CR12) 2000; 2 C Oswald (540_CR44) 2017 540_CR35 J Ziv (540_CR64) 1977; 23 540_CR34 CE Shannon (540_CR54) 2001; 5 ZH Deng (540_CR24) 2014; 41 H Toivonen (540_CR57) 1996; 96 540_CR30 RP Brent (540_CR18) 1987; 19 540_CR15 540_CR58 540_CR16 J Han (540_CR31) 2004; 8 J Ziv (540_CR65) 1978; 24 N Ramakrishnan (540_CR51) 1999; 32 JS Vitter (540_CR59) 1987; 34 540_CR60 540_CR22 540_CR21 540_CR23 JL Bentley (540_CR14) 1986; 29 IH Witten (540_CR61) 1987; 30 540_CR62 MR Nelson (540_CR42) 1989; 14 D Pountain (540_CR48) 1987; 12 540_CR20 540_CR63 540_CR3 540_CR4 540_CR1 540_CR2 J Labeit (540_CR36) 2017; 43 540_CR47 540_CR49 KC Lin (540_CR38) 2014; 276 540_CR9 540_CR7 540_CR8 540_CR5 540_CR6 ZH Deng (540_CR25) 2015; 42 540_CR11 540_CR55 540_CR10 540_CR13 JA Storer (540_CR56) 1982; 29 S Brin (540_CR19) 1997; 26 TV Ramabadran (540_CR50) 1988; 4 540_CR53 |
| 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. In: Data Compression Conference (DCC), 2017, IEEE, pp 350–359 – reference: VitterJSDesign and analysis of dynamic huffman codesJ ACM (JACM)198734482584591384410.1145/31846.422270637.94002 – reference: Köppl D, Sadakane K (2016) Lempel-ziv computation in compressed space (lz-cics). In: Data Compression Conference (DCC), 2016, IEEE, pp 3–12 – reference: RamabadranTVGaitondeSSA tutorial on crc computationsIEEE Micro19884627510.1109/40.7773 – reference: Pei J, Han J, Lu H, Nishio S, Tang S, Yang D (2001) H-mine: Hyper-structure mining of frequent patterns in large databases. In: Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on, IEEE, pp 441–448 – reference: DengZHLvSLPrepost+: an efficient n-lists-based algorithm for mining frequent itemsets via children–parent equivalence pruningExpert Syst Appl201542135424543210.1016/j.eswa.2015.03.004 – reference: BentleyJLSleatorDDTarjanREWeiVKA locally adaptive data compression schemeCommun ACM198629432033083504410.1145/5684.56880648.94007 – reference: OswaldCSrinidhiSVishnuKSVishalTSivaselvanBHash based frequent pattern mining approach to text compressionEAI2017 – reference: ZivJLempelACompression of individual sequences via variable-rate codingIEEE Trans Inf Theory197824553053650746510.1109/TIT.1978.10559340392.94004 – reference: Li W, Yao Y (2016) Accelerate data compression in file system. In: Data Compression Conference (DCC), 2016, IEEE, pp 615–615 – reference: Miller VS, Wegman MN (1985) Variations on a theme by ziv and lempel. In: Combinatorial algorithms on words, Springer, pp 131–140 – reference: StorerJASzymanskiTGData compression via textual substitutionJ ACM (JACM)198229492895167425210.1145/322344.3223460489.68041 – reference: Liu J, Pan Y, Wang K, Han J (2002) Mining frequent item sets by opportunistic projection. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 229–238 – reference: NelsonMRLzw data compressionDr Dobb’s J198914102936 – reference: BorgeltCFrequent item set miningWiley Interdiscip Rev Data Min Knowl Discov20122643745610.1002/widm.1074 – reference: DengZHLvSLFast mining frequent itemsets using nodesetsExpert Syst Appld201441104505451210.1016/j.eswa.2014.01.025 – reference: Agarwal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Bocca JB, Jarke M, Zaniolo C (eds) VLDB’94, Proceedings of 20th International Conference on Very Large Data Bases, September 12–15, 1994. Morgan Kaufmann, Santiago de Chile, Chile, pp 487–499 – reference: (2006) Rar compression algorithm. http://www.rarlab.com/. Accessed 12 Jan 2017 – reference: Zaki MJ, Gouda K (2003) Fast vertical mining using diffsets. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 326–335 – reference: JakobssonMCompression of character strings by an adaptive dictionaryBIT Num Math198525459360310.1007/BF01936138 – reference: (2003) Deflate compression algorithm. http://www.pkware.com. Accessed 12 Jan 2017 – reference: ToivonenHSampling large databases for association rulesVLDB199696134145 – reference: Deorowicz S (2003) Universal lossless data compression algorithms. Philosophy Dissertation Thesis, Gliwice – reference: PountainDRun-length encodingByte1987126317319 – reference: Savasere A, Omicinski ER, Navathe SB (1995) An efficient algorithm for mining association rules in large databases – reference: MoffatAImplementing the ppm data compression schemeIEEE Trans Commun199038111917192110.1109/26.61469 – reference: BastideYTaouilRPasquierNStummeGLakhalLMining frequent patterns with counting inferenceACM SIGKDD Explor Newslett200022667510.1145/380995.3810170983.68511 – reference: Borgelt C (2005) Keeping things simple: finding frequent item sets by recursive elimination. In: Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations, ACM, pp 66–70 – reference: Han J, Kamber M (2000) Data mining: concepts and techniques. Morgan Kaufmann – reference: Bledsoe RE (1987) Data communication with modified Huffman coding – reference: (2014) Amazon reviews dataset. http://jmcauley.ucsd.edu/data/amazon/. Accessed 03 June 2017 – reference: Burrows M, Wheeler DJ (1994) A block-sorting lossless data compression algorithm – reference: David S (2004) Data compression: the complete reference, 2nd edn. http://www.ecs.csun.edu/~dxs/DC3advertis/Dcomp3Ad.html – reference: Decaroli G, Gagie T, Manzini G (2017) A compact index for order-preserving pattern matching. In: Data Compression Conference (DCC), 2017, IEEE, pp 72–81 – reference: BrentRPA linear algorithm for data compressionAust Comput J198719264680641.68029 – reference: HanJPeiJYinYMaoRMining frequent patterns without candidate generation: a frequent-pattern tree approachData Min Knowl Discov2004815387203783110.1023/B:DAMI.0000005258.31418.83 – reference: Goethals B (2003) Survey on frequent pattern mining, manuscript – reference: RodehMPrattVREvenSLinear algorithm for data compression via string matchingJ ACM (JACM)1981281162460318010.1145/322234.3222370462.94015 – reference: Bastide Y, Taouil R, Pasquier N, Stumme G, Lakhal L (2000a) Mining frequent patterns with counting inference. SIGKDD Explor Newsl 2(2):66–75. doi:10.1145/380995.381017 – reference: Pratas D, Pinho AJ, Ferreira PJ (2016) Efficient compression of genomic sequences. In: Data Compression Conference (DCC), 2016, IEEE, pp 231–240 – reference: (2005) TREC genomics track data. http://skynet.ohsu.edu/trec-gen/data/2004/. Accessed 12 Jan 2017 – reference: (2013) Silesia dataset. http://sun.aei.polsl.pl/~sdeor/index.php?page=silesia. Accessed 12 Jan 2017 – reference: DavidA H (1952) A method for the construction of minimum redundancy codes. Proc IRE 40(9):1098–1101 – reference: (2008) PAQ. http://www.cs.fit.edu/~mmahoney/compression. Accessed 02 June 2017 – reference: (2013) Debruijn sequence data. http://bioinf.spbau.ru/en/spadesmanual. Accessed 03 June 2017 – reference: Golomb S (2006) Run-length encodings (corresp.). IEEE Trans Inf Theor 12(3):399–401. doi:10.1109/TIT.1966.1053907 – reference: Oswald C, Ghosh AI, Sivaselvan B (2015) An efficient text compression algorithm-data mining perspective. In: Mining Intelligence and Knowledge Exploration, Springer, pp 563–575 – reference: Basu S, Chaturvedi S, Hegde RM (2016) Text compression using lexicographic permutation of binary strings. In: 2016 International Conference on Signal Processing and Communications (SPCOM), IEEE, pp 1–5 – reference: Zaki MJ (2000) Scalable algorithms for association mining. IEEE Trans Knowl Data Eng 12(3):372–390. http://dblp.uni-trier.de/db/journals/tkde/tkde12.htmlZaki00 – reference: Policriti A, Prezza N (2016) Computing lz77 in run-compressed space. In: Data Compression Conference (DCC), 2016, IEEE, pp 23–32 – reference: HanJChengHXinDYanXFrequent pattern mining: current status and future directionsData Min Knowl Discov20071515586238077710.1007/s10618-006-0059-1 – reference: LinKCLiaoIEChangTPLinSFA frequent itemset mining algorithm based on the principle of inclusion–exclusion and transaction mappingInf Sci201427627828910.1016/j.ins.2014.02.060 – reference: Uno T, Kiyomi M, Arimura H (2005) Lcm ver. 3: Collaboration of array, bitmap and prefix tree for frequent itemset mining. In: Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations, ACM, pp 77–86 – volume: 276 start-page: 278 year: 2014 ident: 540_CR38 publication-title: Inf Sci doi: 10.1016/j.ins.2014.02.060 – volume: 4 start-page: 62 year: 1988 ident: 540_CR50 publication-title: IEEE Micro doi: 10.1109/40.7773 – ident: 540_CR9 – ident: 540_CR16 doi: 10.1145/1133905.1133914 – ident: 540_CR63 doi: 10.1145/956750.956788 – ident: 540_CR5 – volume: 19 start-page: 64 issue: 2 year: 1987 ident: 540_CR18 publication-title: Aust Comput J – volume: 14 start-page: 29 issue: 10 year: 1989 ident: 540_CR42 publication-title: Dr Dobb’s J – ident: 540_CR47 doi: 10.1109/DCC.2016.30 – volume: 8 start-page: 53 issue: 1 year: 2004 ident: 540_CR31 publication-title: Data Min Knowl Discov doi: 10.1023/B:DAMI.0000005258.31418.83 – volume: 25 start-page: 593 issue: 4 year: 1985 ident: 540_CR33 publication-title: BIT Num Math doi: 10.1007/BF01936138 – ident: 540_CR55 – volume: 15 start-page: 55 issue: 1 year: 2007 ident: 540_CR32 publication-title: Data Min Knowl Discov doi: 10.1007/s10618-006-0059-1 – ident: 540_CR2 – ident: 540_CR46 – volume: 12 start-page: 317 issue: 6 year: 1987 ident: 540_CR48 publication-title: Byte – volume: 29 start-page: 928 issue: 4 year: 1982 ident: 540_CR56 publication-title: J ACM (JACM) doi: 10.1145/322344.322346 – ident: 540_CR62 doi: 10.1109/69.846291 – volume: 41 start-page: 4505 issue: 10 year: 2014 ident: 540_CR24 publication-title: Expert Syst Appld doi: 10.1016/j.eswa.2014.01.025 – ident: 540_CR8 – volume: 2 start-page: 437 issue: 6 year: 2012 ident: 540_CR17 publication-title: Wiley Interdiscip Rev Data Min Knowl Discov doi: 10.1002/widm.1074 – ident: 540_CR39 doi: 10.1145/775047.775081 – ident: 540_CR4 – ident: 540_CR58 doi: 10.1145/1133905.1133916 – volume: 30 start-page: 520 issue: 6 year: 1987 ident: 540_CR61 publication-title: Commun ACM doi: 10.1145/214762.214771 – ident: 540_CR43 doi: 10.1007/978-3-319-26832-3_53 – ident: 540_CR20 – volume: 28 start-page: 16 issue: 1 year: 1981 ident: 540_CR52 publication-title: J ACM (JACM) doi: 10.1145/322234.322237 – volume: 29 start-page: 320 issue: 4 year: 1986 ident: 540_CR14 publication-title: Commun ACM doi: 10.1145/5684.5688 – volume: 24 start-page: 530 issue: 5 year: 1978 ident: 540_CR65 publication-title: IEEE Trans Inf Theory doi: 10.1109/TIT.1978.1055934 – ident: 540_CR1 – ident: 540_CR28 – volume: 23 start-page: 337 issue: 3 year: 1977 ident: 540_CR64 publication-title: IEEE Trans Inf Theory doi: 10.1109/TIT.1977.1055714 – ident: 540_CR10 – volume: 96 start-page: 134 year: 1996 ident: 540_CR57 publication-title: VLDB – ident: 540_CR15 – ident: 540_CR7 – ident: 540_CR49 doi: 10.1109/DCC.2016.60 – volume: 32 start-page: 34 issue: 8 year: 1999 ident: 540_CR51 publication-title: IEEE Comput doi: 10.1109/2.781632 – ident: 540_CR3 – ident: 540_CR21 – ident: 540_CR45 doi: 10.1145/568271.223813 – ident: 540_CR35 doi: 10.1109/DCC.2016.38 – volume: 43 start-page: 2 year: 2017 ident: 540_CR36 publication-title: J Disc Algorithms doi: 10.1016/j.jda.2017.04.001 – ident: 540_CR37 doi: 10.1109/DCC.2016.24 – volume: 2 start-page: 66 issue: 2 year: 2000 ident: 540_CR12 publication-title: ACM SIGKDD Explor Newslett doi: 10.1145/380995.381017 – ident: 540_CR30 – ident: 540_CR53 – ident: 540_CR23 doi: 10.1109/DCC.2017.35 – volume: 38 start-page: 1917 issue: 11 year: 1990 ident: 540_CR41 publication-title: IEEE Trans Commun doi: 10.1109/26.61469 – ident: 540_CR29 doi: 10.1109/TIT.1966.1053907 – volume: 26 start-page: 255 year: 1997 ident: 540_CR19 publication-title: ACM SIGMOD Record ACM doi: 10.1145/253262.253325 – ident: 540_CR6 – volume: 42 start-page: 5424 issue: 13 year: 2015 ident: 540_CR25 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2015.03.004 – ident: 540_CR22 – year: 2017 ident: 540_CR44 publication-title: EAI doi: 10.4108/eai.27-2-2017.152268 – volume: 34 start-page: 825 issue: 4 year: 1987 ident: 540_CR59 publication-title: J ACM (JACM) doi: 10.1145/31846.42227 – ident: 540_CR34 doi: 10.1109/DCC.2017.73 – ident: 540_CR40 doi: 10.1007/978-3-642-82456-2_9 – volume: 5 start-page: 3 issue: 1 year: 2001 ident: 540_CR54 publication-title: ACM SIGMOBILE Mobile Comput Commun Rev doi: 10.1145/584091.584093 – ident: 540_CR13 doi: 10.1109/SPCOM.2016.7746651 – ident: 540_CR26 – ident: 540_CR11 doi: 10.1145/380995.381017 – ident: 540_CR27 doi: 10.1109/DCC.2016.70 – ident: 540_CR60 doi: 10.1109/DCC.1991.213344 |
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