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Musical Data Mining Pattern Matching Apriori and DHP Algorithm

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IJCA Proceedings on National Conference on Advance Trends in Information Technology
© 2013 by IJCA Journal
NCATIT - Number 1
Year of Publication: 2013
Authors:
J. James Alaguraja

James J Alaguraja. Article: Musical Data Mining Pattern Matching Apriori and DHP Algorithm. IJCA Proceedings on National Conference on Advance Trends in Information Technology NCATIT:16-19, April 2013. Full text available. BibTeX

@article{key:article,
	author = {J. James Alaguraja},
	title = {Article: Musical Data Mining Pattern Matching Apriori and DHP Algorithm},
	journal = {IJCA Proceedings on National Conference on Advance Trends in Information Technology},
	year = {2013},
	volume = {NCATIT},
	pages = {16-19},
	month = {April},
	note = {Full text available}
}

Abstract

Musical data mining is not a new invention, but as a nation-wide resource of this type it breaks new ground by providing researchers with new ways to analyze musical data. It was Toiviainen and Eerola´s idea to combine specific information with a geographical coordinate database. Now geographical comparisons can be made it is possible to follow the geographical variation of musical features. For instance, schools can now identify and trace folk tune originating from their own regions. Musical Data Mining is used for discovering any kind of relevant similarity between music titles. Several algorithms like Apriori, PHP, partition, sampling and some other parallel algorithm have been developed. In this thesis, Apriori and DHP are implemented. To extract the similarity between music titles and to manipulate their relationships two techniques are used co-occurrence analysis and correlation analysis. By the use of these two techniques it is capable to access the database and then find whether any similarity exist between the music titles. For the purpose of finding a match within the titles in the database Pattern matching is used using the Apriori and DHP algorithms

References

  • D. Pyle, Data Preparation for Data Mining. San Francisco, CA Morgan Kaufmann, 1999.
  • Munz, Matt. Data Mining in Musicology. Yale University. 2005.
  • R. Agrawal, T. Imielinski, and A. Swami, "Database Mining: A Performance Perspective," IEEE Trans. Knowledge and Data Eng. ,vol. 5, no. 6, Dec. 1993.
  • Pachet, Francois, Gert Westermann, and Damien Laigre. "Musical Data Mining for Electronic Music Distribution". 1st International Conference on Web Delivering of Music. 2001.
  • Pavankumar Bondugula, Implementation and Analysis of Apriori Algorithm for Data Mining

Keywords