標題: Online mining maximal frequent structures in continuous landmark melody streams
作者: Li, HF
Lee, SY
Shan, MK
資訊工程學系
Department of Computer Science
關鍵字: machine learning;data mining;landmark melody stream;maximal melody structure;Online algorithm
公開日期: 1-八月-2005
摘要: In this paper, we address the problem of online mining maximal frequent structures (Type I & II melody structures) in unbounded, continuous landmark melody streams. An efficient algorithm, called MMSLMS (Maximal Melody Structures of Landmark Melody Streams), is developed for online incremental mining of maximal frequent melody substructures in one scan of the continuous melody streams. In MMSLMS, a space-efficient scheme, called CMB (Chord-set Memory Border), is proposed to constrain the upper-bound of space requirement of maximal frequent melody structures in such a streaming environment. Theoretical analysis and experimental study show that our algorithm is efficient and scalable for mining the set of all maximal melody structures in a landmark melody stream. (c) 2005 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.patrec.2005.01.016
http://hdl.handle.net/11536/13438
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2005.01.016
期刊: PATTERN RECOGNITION LETTERS
Volume: 26
Issue: 11
起始頁: 1658
結束頁: 1674
顯示於類別:期刊論文


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  1. 000230052300006.pdf