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學術出版;;Publications

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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

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    A new probabilistic induction method
    (1997-02-01)
    Hou, RH
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    Hong, TP
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    Tseng, SS
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    Kuo, SY
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    資訊工程學系
    Knowledge acquisition by interviewing a domain expert is one of the most problematic aspects of the development of expert systems. As an alternative, methods for inducing concept descriptions from examples have proven useful in eliminating this bottleneck. In this paper, we propose a probabilistic induction method (PIM), which is an improvement of the Chan and Wong method, for detecting relevant patterns implicit in a given data set. PIM uses the technique of residual analysis and several heuristics to effectively detect complex relevant patterns and to avoid the problem of combinatorial explosion. A reasonable trade-off between the induction time and the classification ratio is achieved. Moreover, PIM quickly classifies unknown objects using classification rules converted from the positively relevant patterns detected. Three experiments are conducted to confirm the validity of PIM.
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    A new hybrid learning algorithm for non-linear boundaries
    (1998-06-01)
    Wang, CH
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    Hong, TP
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    Tseng, SS
    ;
    資訊工程學系
    ;
    Department of Computer Science
    In this paper, we propose a new hybrid learning algorithm, ETNC, which incorporates the popular decision-tree learning algorithm ASSISTANT into a modified three-layer back-propagation learning method to construct an entropy-tree net classifier. The new Iearning algorithm also adopts a tree-pruning mechanism to avoid overfitting problems. The new algorithm decreases both the tree size and error rate, especially for complex classification problems. Furthermore, it is not necessary for users to lay out the structure of a tree net in advance; instead, the structure is automatically constructed in the tree-growing process. Finally, the results of experiments in diagnosing brain tumors and classifying sugar canes are described to compare the proposed algorithm with two other learning methods, the back-propagation learning algorithm and ASSISTANT, in terms of accuracy, knowledge complexity and learning speed. Experimental results show that the proposed learning algorithm can provide a good trade-off between accuracy, knowledge complexity and learning speed.
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    Primal-dual version spaces for acquisition of disjunctive concepts
    (1998-06-01)
    Hong, TP
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    Tseng, SS
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Among incremental learning strategies, the "version space" learning strategy is one of the most well known. This learning strategy is, however, applicable only to learning conjunctive concepts. When the concepts to be learned are in disjunctive form, the version space learning strategy returns a null Version space that cannot correctly represent the desired concepts. In this paper, we present a modification of the original version space strategy that enables learning of disjunctive concepts. The new proposed version-space-based learning strategy, called the "primal-dual version-spaces" learning strategy, learns disjunctive concepts incrementally and without saving past training instances. The correctness of its underlying algorithm is analyzed and proven.
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    Inductive learning from fuzzy examples
    (1996)
    Wang, CH
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    Hong, TP
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    Tseng, SS
    ;
    資訊工程學系
    ;
    Department of Computer Science
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    FILSMR: A fuzzy inductive learning strategy for modular rules
    (1997)
    Wang, CH
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    Liu, JF
    ;
    Hong, TP
    ;
    Tseng, SS
    ;
    交大名義發表
    In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. The design of learning methods to learn concept descriptions in linguistic environments is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the maximum information gain is proposed to manage linguistic information. Experiments on the sport classification problem are to demonstrate the effectiveness of the proposed algorithm. Experimental results show that the rules derived from our approach are simpler and yields high accuracy.
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    Maintenance of sequential patterns for record deletion
    (2001)
    Wang, CY
    ;
    Hong, TP
    ;
    Tseng, SS
    ;
    交大名義發表
    ;
    National Chiao Tung University
    In the past, we proposed an incremental mining algorithm for maintenance of sequential patterns based on the concept of pre-large sequences as new records were inserted. In this paper, we attempt to apply the concept of pre-large sequences to maintain sequential patterns as records are deleted. Pre-large sequences are defined by a lower support threshold and an upper support threshold. They, act as buffers to avoid the movements of sequential patterns directly from large to small and vice-versa. Our proposed algorithm does not require rescanning original databases until the accumulative amount of deleted customer sequences exceeds a safety bound, which depends on database size. As databases grow larger, the numbers of deleted customer sequences allowed before database rescanning is required also grow. The proposed approach is thus efficient for a large database.
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    Maintenance of sequential patterns for record modification using pre-large sequences
    (2002)
    Wang, CY
    ;
    Hong, TP
    ;
    Tseng, SS
    ;
    交大名義發表
    ;
    National Chiao Tung University
    In the past, we proposed incremental mining algorithms for maintenance of sequential patterns based on the concept of pre-large sequences as records were inserted or deleted Although maintenance of sequential patterns for record modification can be performed by usage of the deletion procedure and then the insertion procedure, twice computation time of a single procedure is needed In this paper, we thus attempt to apply the concept of pre-large sequences to maintain sequential patterns as records are modified. The proposed algorithm does not require rescanning original databases until the accumulative amount of modified customer sequences exceeds a safety bound derived by pre-large concept. As databases grow larger, the numbers of modified customer sequences allowed before database rescanning is required also grow.
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    A three-phased online association rule mining approach for diverse mining requests
    (2004)
    Wang, CY
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    Tseng, SS
    ;
    Hong, TP
    ;
    Chu, YS
    ;
    資訊工程學系
    In the past, most incremental mining and online mining algorithms considered finding the set of association rules or patterns consistent with the entire set of data inserted so far. Users can not easily obtain the results from their only interested portion of data. For providing ad-hoc, query-driven and online mining supports, we first propose a relation called multidimensional pattern relation to structurally and systematically store the context information and the mining information for later analysis. Each tuple in the relation comes from an inserted dataset in the database. This concept is similar to the construction of a data warehouse for OLAP. However, unlike the summarized information of fact attributes in a data warehouse, the mined patterns in the multidimensional pattern relation can not be directly aggregated to satisfy, users' mining requests. We then develop an online mining approach called Three-phased Online Association Rule Mining (TOARM) based on the proposed multidimensional pattern relation to support online generation of association rules under multidimensional considerations. Experiments for both homogeneous and heterogeneous datasets are made, with results showing the effectiveness of the proposed approach.
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    Multidimensional on-line mining
    (2006)
    Wang, CY
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    Hong, TP
    ;
    Tseng, SS
    ;
    資訊工程學系
    ;
    Department of Computer Science
    In the past;, incremental mining approaches usually considered getting the newest; set; of knowledge consistent; with the entire set of data inserted so far. Users can not, however, use them to obtain rules or patterns only from their interesting portion of the data. In addition, these approaches only focused on finding frequent patterns in a specified part of a database. That is, although the data records are collected in under certain time, place and category, such contexts (circumstances) have been ignored in conventional mining algorithms. It will cause the lack of patterns or rules to help users solve problems at, different, aspects and with diverse considerations. In this paper, we thus attempt; to extend incremental mining to online decision support under multidimensional context considerations. We first propose the multidimensional pattern relation to structurally and systematically retain the additional context information and mining information for each inserted dataset into a database. We then develop an algorithm based on the proposed multidimensional pattern relation to correctly and efficiently fulfill diverse on-fine mining requests.
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    Improved negative-border online mining approaches
    (2006)
    Wang, CY
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    Tseng, SS
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    Hong, TP
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    資訊工程學系
    ;
    Department of Computer Science
    In the past, we proposed an extended multidimensional pattern relation (EMPR) to structurally and systematically store previously mining information for each inserted block of data, and designed a negative-border online mining (NOM) approach to provide ad-hoc, query-driven and online mining supports. In this paper, we try to use appropriate data structures and design efficient algorithms to improve the performance of the NOM approach. The lattice data structure is utilized to organize and maintain all candidate itemsets such that the candidate itemsets with the same proper subsets can be considered at the same time. The derived lattice-based NOM (LNOM) approach will require only one scan of the itemsets stored in EMPR, thus saving much computation time. In addition, a hashing technique is used to further improve the performance of the NOM approach since many itemsets stored in EMPR may be useless for calculating the counts of candidates. At last, experimental results show the effect of the improved NOM approaches.