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

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Musical Data