Research On Clustering Algorithms In Traffic Domain

With the development of information technology, the data stored in database of all fields becomes more and more, and simple query and statistic methods are not enough now. Providing the proof for high management and assistant decision is the key of solving problem, which makes use of data mining for discovering potential and meaningful rules from existing data and obtains valuable knowledge. Therefore, the "Research on Clustering Algorithms and Their Application in Traffic Domain" is proposed in this dissertation, which can be shown as follows:1. Integration methods for complexity, isomerism and multiple sources data, this method adopts XML technology to implement the interface of data interchange and provides data sharing and exchange, and solves the problem of data isomerism among existing systems in any field. Then it can implement data interconnection and mutual communication and prepare the data for data mining.2. Weighted entropy fuzzy c-means optimization method for mixed numerical and categorical data, which is proposed for overcoming the disadvantages of existing algorithms. Then it is introduced into fuzzy association rules, which improves the accuracy and efficiency of association rule algorithm and broadens the application range of association rule.3. Study clustering ensemble algorithm for mixed numerical and categorical data, this algorithm is able to increase the stability, accuracy and efficiency of clustering. The structure of clustering ensemble models is given in this dissertation, and then we expands the models for mixed numerical and categorical data, including the methods of producing clustering memberships for categorical data and mixed data, algorithms and steps of designing integration functions, and merging and dividing strategies and its procedure.4. Incremental clustering algorithm for mixed numerical and categorical data based on clustering ensemble. The algorithm is proposed for solving problems that research on incremental clustering algorithms is little and existing incremental clustering algorithms is often unstable. Then the incremental clustering algorithms with history data and without history data are discussed respectively, which increase the accuracy and efficiency of clustering, and reduce the clustering time.5. Application of clustering analysis in traffic domain, it mines the reasons and potential rules leading to traffic accidents and aids decision making for related management departments, which can be used to prevent the occurrence of traffic accidents and guarantee the safety of the nation and people's lives and property. The algorithm improves the management efficiency of maritime management organizations and provides proof of decision making by clustering applied in partitioning ship ranks.