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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Tseng, Vincent S.Has files: Yes

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    Effective Semantic Annotation by Image-to-Concept Distribution Model
    (2011-06-01)
    Su, Ja-Hwung
    ;
    Chou, Chien-Li
    ;
    Lin, Ching-Yung
    ;
    Tseng, Vincent S.
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    資訊工程學系
    Image annotation based on visual features has been a difficult problem due to the diverse associations that exist between visual features and human concepts. In this paper, we propose a novel approach called Annotation by Image-to-Concept Distribution Model (AICDM) for image annotation by discovering the associations between visual features and human concepts from image-to-concept distribution. Through the proposed image-to-concept distribution model, visual features and concepts can be bridged to achieve high-quality image annotation. In this paper, we propose to use "visual features", "models", and "visual genes" which represent analogous functions to the biological chromosome, DNA, and gene. Based on the proposed models using entropy, tf-idf, rules, and SVM, the goal of high-quality image annotation can be achieved effectively. Our empirical evaluation results reveal that the AICDM method can effectively alleviate the problem of visual-to-concept diversity and achieve better annotation results than many existing state-of-the-art approaches in terms of precision and recall.
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    Item type:Item,
    An efficient algorithm for mining temporal high utility itemsets from data streams
    (2008-07-01)
    Chu, Chun-Jung
    ;
    Tseng, Vincent S.
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    Liang, Tyne
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Utility of an itemset is considered as the value of this itemset, and utility mining aims at identifying the itemsets with high utilities. The temporal high utility itemsets are the itemsets whose support is larger than a pre-specified threshold in current time window of the data stream. Discovery of temporal high utility itemsets is an important process for mining interesting patterns like association rules from data streams. In this paper, we propose a novel method, namely THUI (Temporal High Utility Itemsets)-Mine, for mining temporal high utility itemsets from data streams efficiently and effectively. To the best of our knowledge, this is the first work on mining temporal high utility itemsets from data streams. The novel contribution of THUI-Mine is that it can effectively identify the temporal high utility itemsets by generating fewer candidate itemsets such that the execution time can be reduced substantially in mining all high utility itemsets in data streams. In this way, the process of discovering all temporal high utility itemsets under all time windows of data streams can be achieved effectively with less memory space and execution time. This meets the critical requirements on time and space efficiency for mining data streams. Through experimental evaluation, THUI-Mine is shown to significantly outperform other existing methods like Two-Phase algorithm under various experimental conditions. (c) 2007 Elsevier Inc. All rights reserved.
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    Item type:Item,
    Efficient mining of temporal emerging itemsets from data streams
    (2009-01-01)
    Chu, Chun-Jung
    ;
    Tseng, Vincent S.
    ;
    Liang, Tyne
    ;
    資訊工程學系
    ;
    Department of Computer Science
    In this paper, we propose a new method, namely EFI-Mine, for mining temporal emerging frequent itemsets from data streams efficiently and effectively. The temporal emerging frequent itemsets are those that are infrequent in the current time window of data stream but have high potential to become frequent in the subsequent time windows. Discovery of emerging frequent itemsets is an important process for mining interesting patterns like association rules from data streams. The novel contribution of EFI-Mine is that it can effectively identify the potential emerging itemsets such that the execution time can be reduced substantially in mining all frequent itemsets in data streams. This meets the critical requirements of time and space efficiency for mining data streams. The experimental results show that EFI-Mine can find the emerging frequent itemsets with high precision under different experimental conditions and it performs scalable in terms of execution time. (C) 2007 Elsevier Ltd. All rights reserved.
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    Item type:Item,
    An efficient algorithm for mining high utility itemsets with negative item values in large databases
    (2009-09-15)
    Chu, Chun-Jung
    ;
    Tseng, Vincent S.
    ;
    Liang, Tyne
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Utility itemsets typically consist of items with different values such as utilities, and the aim of utility mining is to identify the itemsets with highest utilities. In the past studies on utility mining, the values of utility itemsets were considered as positive. In some applications, however, an itemset may be associated with negative item values. Hence, discovery of high utility itemsets with negative item values is important for mining interesting patterns like association rules. In this paper, we propose a novel method, namely HUINIV (High Utility Itemsets with Negative Item Values)-Mine, for efficiently and effectively mining high utility itemsets from large databases with consideration of negative item values. To the best of our knowledge, this is the first work that considers the concept of negative item values in utility mining. The novel contribution of HUINIV-Mine is that it can effectively identify high utility itemsets by generating fewer high transaction-weighted utilization itemsets such that the execution time can be reduced substantially in mining the high utility itemsets. In this way, the process of discovering all high utility itemsets with consideration of negative item values can be accomplished effectively with less requirements on memory space and CPU I/O. This meets the critical requirements of temporal and spatial efficiency for mining high utility itemsets with negative item values. Through experimental evaluation, it is shown that HUINIV-Mine outperforms other methods substantially by generating much less candidate itemsets under different experimental conditions. (C) 2009 Elsevier Inc. All rights reserved.