Browsing by Author "To, Tang Van"
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ItemClustering approach to examination scheduling( 2010-05) To, Tang Van ; Win, Soe SanThe difficulty in examination scheduling is to draw an examination timetable by taking a number of different constraints into account. This paper attempts to optimize two major constraints-to minimize the examination time conflicts and the number of consecutive examinations for a student in a day. Clustering method is applied by analyzing exam enrollments and then groups students into clusters so that students within a cluster are having more similar subject combination to each others than those in another clusters. The purpose of clustering is to effectively arrange the order of exams to be allocated into a suitable exam period. Since exams are scheduled by clusters, it solves the exam conflicts for the students within the same cluster whose are closely related to each other.
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ItemLinear recurrence equation to analyzing the complexity of algorithms( 2009) To, Tang VanSolving recurrence equation (Res) is an important technique in the analysis of algorithms. Especially for the divide and conquer algorithms, establishing the recurrence equations, solving them as well as finding the order of complexity will be discussed.
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ItemA Self-Growing and Self-Organizing Batch Map with Automatic Stopping Condition( 2013) Kim, Se Won ; To, Tang VanThis paper proposes a model of self-growing and self-organizing feature map designed to alleviate the difficulty of predetermining an appropriate size and shape of the feature map suitable for the input data in the applications of the Self-Organizing Map. The proposed model progressively builds a feature map by incremental growing of the network in a way that maintains two-dimensional regular grid structure and gradual adaptation of the reference vectors by coordinated competitive learning dynamics of the Batch Map algorithm. Experimental results based on iris data set and Italian olive oil data set show that the proposed model is effective in discovering an appropriate size and shape of the network grid to manifest a suitable feature map for the input data and that the resultant feature maps are comparable to feature maps produced by the standard SOM algorithm in their quality.
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