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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Chen, Hsuan-ShengSubject: search.filters.subject.Data mining

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    Incorporating frequent pattern analysis into multimodal HMM event classification for baseball videos
    (2016-05)
    Chen, Hsuan-Sheng
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    Tsai, Wen-Jiin
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    資訊工程學系
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    Department of Computer Science
    Data mining and frequent pattern analysis have recently become a popular way of discovering new knowledge from a data set. However, it is rarely applied to video semantic analysis. Therefore, this paper introduces two methods: frequent-pattern trained HMM and frequent-pattern tailored HMM to incorporate frequent pattern analysis into multimodal HMM event classification for baseball videos. Besides, different symbol coding methods including temporal sequence coding and co-occurrence symbol coding for multimodal HMM classification are compared. The results of our experiments on baseball video event classification demonstrate that integration of frequent pattern analysis could help to improve event classification performances.
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    DISCOVERING EMERGING MELODY PATTERNS FROM CUSTOMER QUERY DATA STREAMS OF MUSIC SERVICE
    (2011)
    Li, Hua-Fu
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    Chen, Hsuan-Sheng
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Mining melody structure patterns from music query data is one of the most interesting issues of multimedia data mining. In this paper, we introduce a new kind of melody structure pattern, called emerging melody pattern (EMP), for knowledge discovery from music query data streams. EMPs are defined as music data items with melody item-sets whose support increase significantly from one sliding window to another window from streaming melody data sequences. An efficient data mining algorithm, called MEMPA (Mining Emerging Melody Pattern Algorithm), is proposed to discover all EMPs from music query data. In the framework of MEMPA, a prefix tree-based structure, called EMP-tree (Emerging Melody Pattern tree), is developed for storing EMPs effectively from current stream sliding windows. Experimental results show that the proposed algorithm is an efficient method for discovering all EMPs from streaming melody sequences.
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    A SINGLE-SCAN ALGORITHM FOR MINING SEQUENTIAL PATTERNS FROM DATA STREAMS
    (2012-03-01)
    Li, Hua-Fu
    ;
    Ho, Chin-Chuan
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    Chen, Hsuan-Sheng
    ;
    Lee, Suh-Yin
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    資訊工程學系
    Sequential pattern mining (SPAM) is one of the most interesting research issues of data mining. In this paper, a new research problem of mining data streams for sequential patterns is defined. A data stream is an unbound sequence of data elements arriving at a rapid rate. Based on the characteristics of data streams, the problem complexity of mining data streams for sequential patterns is more difficult than that of mining sequential patterns from large static databases. Therefore, mining sequential patterns from data streams is a challenging research issue of data mining and knowledge discovery. Hence, an efficient single-pass algorithm, called IncSparn (Incremental Sequential pattern mining of streaming itemset-sequences), is proposed for discovering sequential patterns from streaming itemset-sequences over extended sliding window models. In the framework of IncSpam algorithm, a new sliding window model, called CSW-BV (Customer Sliding Window with Bit-Vectors), and an extended lexicographic tree-based data structure, called LesSeq-Tree (Lexicographic Sequence Tree), are developed to reduce the time and memory needed to slide the windows over streaming data and maintain all sequential patterns of current sliding windows. Experimental results show that the proposed method is an efficient single-pass algorithm for mining sequential patterns from streaming data.