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Semi-supervised Clustering Using Degree and Its Application in the Short-term Forecasting of Export-container Quantity

Author: LiZuo
Tutor: WangXinWei
School: East China Normal University
Course: System Analysis and Integration
Keywords: Data Mining Semi-supervised clustering Hidden Markov Models ARMA model Short-term forecasts of container volume
CLC: TP311.13
Type: Master's thesis
Year: 2006
Downloads: 144
Quote: 2
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Abstract


Data mining from large amounts of data to extract information and knowledge of the people are interested, these are often implicit, useful information and knowledge not yet discovered. Currently, data mining has caused widespread concern, the most cutting-edge research direction of domestic and international databases and information areas of decision-making. Clustering is one of the most commonly used technology in the field of data mining. Will existing data objects divided into different collection, making it divided into the same collection of data objects are similar, divided into different sets of data objects, relatively speaking, a greater difference. Clustering process usually without the guidance of teachers, so an unsupervised classification. With depth clustering Research, the importance of background knowledge for cluster analysis gradually recognized. The tendency of the user into the cluster analysis process to be a challenging problem. First probabilistic model for semi-supervised clustering, in-depth study and detailed analysis of the existing algorithms for existing algorithms ignore the negative association limit the role and algorithm complexity higher lack a degree of semi-supervised clustering algorithm (SCUD). Comprehensive utilization of the algorithm given the positive correlation and negative correlation constraints in the form of background knowledge, according to the degree of constraint to initialize the K cluster centers, and then use the EM algorithm clustering results. As you can see from the experimental results and analysis, the algorithm simply use a relatively small constraint data will be able to get a good clustering effect, and when the number of data objects can be more good clustering effect under the premise of has a smaller time complexity. Practical application characteristics, requirements, and the content of the short-term forecasts for export container volume container port, design the framework of the two-step prediction; proposed prediction-based the SCUD semi-supervised clustering algorithm in this framework. The algorithm could use the next port, the maximum load of the ship, arrival and departure time live in January, and the associated constraints background knowledge to effectively improve the prediction accuracy, port managers for the yard containers stacked tire crane and crane operations and scheduling in advance to make rational planning. So that the container terminal to ensure container quickly approaching, stacking and loading, and shorten the time ship port, to ensure the security and stability of the pier running, to achieve the lowest operating costs of the pier market.

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