Research And Application Of Data Mining In Traffic Flow Data Processing

Data mining is a process that can derive information from abundant data which is disintegrated, disturbed, fuzzy and random. The information always is potentially useful but unknowned for us. It is an interdisciplinary reaching field, which involved database technique, AI, machine learning, statistics, knowledge engineering, OPP method, information searching, high performance computing, data visualization and so on. This subject creates many new concepts and methods in recent dozens of years.Data mining arithmetic includes sorting algorithm, clustering algorithm, arithmetic based on the order of time, association rule mining algorithm and so on. This paper mainly focuses on clustering algorithm and arithmetic based on the order of time. It systemically explains the classic theory and application methods of the two arithmetic mentioned above.Traffic flow data is very important at the field of ITS (Intelligent Transportation Systems). It is always difficult to deal with traffic data intelligently. Core content of this paper is composed with three parts. At first, it is necessary to dispose of data in order to mask it workable for data mining arithmetic. Then it makes use of k-means arithmetic to assort the fee stations and verifies the result by SPSS Climentine11.1 clustering algorithm. Finally, Microsoft algorithm based on the order of time is taken to forecast traffic flow data. The experience proves that accuracy rate of forecasting overruns 80%.