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2013 IEEE 13th International Conference on Data Mining Workshops (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
ISBN: 0-7695-2702-7
pp: 213-217
Elizabeth Chang , Curtin University of Technology Perth, Australia
Fedja Hadzic , University of Technology Sydney, Australia
Henry Tan , University of Technology Sydney, Australia
Amandeep S. Sidhu , University of Technology Sydney, Australia
Tharam S. Dillon , University of Technology Sydney, Australia
ABSTRACT
In this paper we consider the ?Prions? database that describes protein instances stored for Human Prion Proteins. The Prions database can be viewed as a database of rooted ordered labeled subtrees. Mining frequent substructures from tree databases is an important task and it has gained a considerable amount of interest in areas such as XML mining, Bioinformatics, Web mining etc. This has given rise to the development of many tree mining algorithms which can aid in structural comparisons, association rule discovery and in general mining of tree structured knowledge representations. Previously we have developed the MB3 tree mining algorithm, which given a minimum support threshold, efficiently discovers all frequent embedded subtrees from a database of rooted ordered labeled subtrees. In this work we apply the algorithm to the Prions database in order to extract the frequently occurring patterns, which in this case are of induced subtree type. Obtaining the set of frequent induced subtrees from the Prions database can potentially reveal some useful knowledge. This aspect will be demonstrated by providing an analysis of the extracted frequent subtrees with respect to discovering interesting protein information. Furthermore, the minimum support threshold can be used as the controlling factor for answering specific queries posed on the Prions dataset. This approach is shown to be a viable technique for mining protein data.
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CITATION
Elizabeth Chang, Fedja Hadzic, Henry Tan, Amandeep S. Sidhu, Tharam S. Dillon, "Mining Substructures in Protein Data", 2013 IEEE 13th International Conference on Data Mining Workshops, vol. 00, no. , pp. 213-217, 2006, doi:10.1109/ICDMW.2006.114
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