政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/15013
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    题名: Online Mining Maximal Frequent Structures in Continuous Landmark Melody Streams
    作者: 李素瑛;沈錳坤
    Li, Hua-Fu;Lee, Suh-Yin;Shan, Man-Kwan
    关键词: Machine learning;Data mining;Landmark melody stream;Maximal melody structure;Online algorithm
    日期: 2005-11
    上传时间: 2008-12-16 16:47:54 (UTC+8)
    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.
    關聯: Pattern Recognition Letters, 26(11), 1658-1674
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1016/j.patrec.2005.01.016
    DOI: 10.1016/j.patrec.2005.01.016
    显示于类别:[資訊科學系] 期刊論文

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