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Autor
Ożdżyński Piotr (Lodz University of Technology, Poland), Zakrzewska Danuta (Lodz University of Technology, Poland)
Tytuł
Using Frequent Pattern Mining Algorithms in Text Analysis
Źródło
Information Systems in Management, 2017, vol. 6, nr 3, s. 213-222, rys., bibliogr. 17 poz.
Systemy Informatyczne w Zarządzaniu
Słowa kluczowe
Algorytmy, Analiza tekstu
Algorithms, Text analysis
Uwagi
summ.
Abstrakt
In text mining, effectiveness of methods depends on document representations. The ones based on frequent word sequences are used in such tasks as categorization, clustering and topic modelling. In the paper a comparison of different algorithms for finding frequent word sequences is presented. There are considered techniques dedicated for market basket analysis such as GSP and PrefixSpan as well as a method based on a suffix array. The investigated techniques are compared with the new approach of searching maximum frequent word sequences in document sets. Performance of the algorithms is examined taking into account execution times for the considered test collections. (original abstract)
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Bibliografia
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  1. Manning Ch. D., Raghavan P., Schütze H. (2008) An Introduction to Information Retrieval, Cambridge University Press.
  2. Robertson S., Zaragoza H. (2009) The Probabilistic Relevance Framework: BM25 and Beyond, Found. Trends Inf. Retr, 3(4), 333-389.
  3. Burges Ch. J. C. (1998) A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, 2, 121-167.
  4. Zhong N., Li Y., Wu Sh.-T. (2012) Effective Pattern Discovery for Text Mining, IEEE Transactions on Data Engineering, 24(1), 30-44.
  5. Aggarwal Ch. C., Han J. [eds.] (2014) Frequent Pattern Mining, Springer International Publishing Switzerland.
  6. Garcia-Hernández R.A., Martínez-Trinidad J.F., Carrasco-Ochoa J.A. (2010) Finding maximal sequential patterns in text document collections and single documents, Informatica, 34, 93-101.
  7. Ahonen-Myka H. (2002) Discovery of frequent word sequences in text, Proc. the ESF Exploratory Workshop on Pattern Detection and Discovery, London, UK, 180-189.
  8. Ożdżyński P., Zakrzewska D. (2017) Topic Modeling Based on Frequent Sequences Graphs, Świątek J., Tomczak J.M. (eds.), Advances in Systems Science, Advances in Intelligent Systems and Computing 539, Springer International Publishing, 86-97.
  9. Agrawal, R., Srikant R. (1994) Fast algorithms for mining association rules in large databases, Proc. the 20th International Conference on Very Large Data Bases, VLDB, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 487-499.
  10. Agrawal R., Srikant R. (1995) Mining sequential patterns, Proc. 1995 Int. Conf. Data Engineering (ICDE'95), 3-14.
  11. Slimani T., Lazzez A., (2013) Sequential Mining: Patterns and Algorithms Analysis, International Journal of Computer and Electronics Research, 2 (5), 639-647.
  12. Pei J., Han J., Mortazavi-Asl J., Pinto H., Chen Q., Dayal U., Hsu M. (2001) PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth, Proc. 2001 Int. Conf. Data Engineering (ICDE '01), 215-224.
  13. Manber U., Myers G. (1989) Suffix arrays: A new method for on-line string searches, SODA '90 Proc. the first ACM-SIAM symposium on Discrete algorithms, 319-327.
  14. Ożdżyński P. (2014) Text Document Categorization Based on Word Frequent Sequence Mining, Information Systems Architecture and Technology, Contemporary Approaches to Design and Evaluation of Information Systems, 129-138.
  15. ftp://medir.ohsu.edu/pub/ohsumed
  16. http://www.ai.mit.edu/people/jrennie/20Newsgroups/
  17. Fournier-Viger, P., Lin, C.W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., Lam, H.T. (2016). The SPMF Open-Source Data Mining Library Version 2. Proc. PKDD 2016 Part III, Springer LNCS 9853, 36-40.
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ISSN
2084-5537
Język
eng
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