Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-10-11 Cooperative journals: 《计算机应用研究》
Abstract： Association rules are attracting wide attention in the field of big data mining. The key and difficult point of the algorithm is to mine frequent sets. In order to further improve the speed of the association rules mining frequent sets and optimize the execution performance of the algorithm, an association rule mining algorithm based on improved memory structure is proposed. For the existing algorithm, the storage structure is simple, the candidate set with a large amount of redundancy is generated, the time and space complexity is high, and the mining efficiency is not ideal. The algorithm of this paper is based on the Spark distributed framework. The partitions are mined in parallel to extract frequent sets. It is proposed to use the Bloom filter to store the project in the mining process, and to simplify the operation of the transaction set and the candidate set, so as to optimize the speed of mining frequent sets. Save computing resources. Compared with the YAFIM algorithm and the MRApriori algorithm, the algorithm has a significant improvement in the efficiency of mining frequent sets under the condition of occupying less memory. The algorithm can not only improve the mining speed, reduce the memory pressure, but also has good scalability, so that the algorithm can be applied to larger data sets and clusters, so as to optimize the performance of the algorithm.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-06-19 Cooperative journals: 《计算机应用研究》
Abstract： To solve the problems of the zero-watermarking algorithms with weak against geometric attack, this paper proposed a zero-watermarking algorithm against geometric rotation attack. Firstly, the algorithm determined the near-destructive maximum inscribed square region with approximately pixels lossless in the center area, according to the pixel distortion of the image of the scale-invariant feature transform (SIFT) rotation correction. Then, the square region performed two-level redundant discrete wavelet transformation and extracted the low-frequency region. What’s more, it extracted the largest singular value of each block to construct a transition matrix into block processing from the low-frequency area. Next, to obtain a characteristic matrix, it compared the value of each element in the transition matrix with its average value. Finally, it used the watermark image and constructed the characteristic matrix a zero watermark. The experimental results show that compared with the only rotation correction algorithm of SIFT, the robustness against rotation attack is up by an average of 13.26%. Compare with the rotation corrections algorithms of the GH rotation moment and pseudo-Zernike orthogonal moment, the anti-rotation attack robustness is up by 1.1% and 0.94% respectively. Also, it has a good effect on common conventional attacks, scaling attacks, cyclic translation and small-scale shear attacks.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-20 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problem of hyperspectral image collaborative representation anomaly detection algorithm that the output of the central pixel is small and difficult to distinguish from the background when dual window center pixel is anomalous pixel and background dictionary contains the same kind of anomalous pixels. This paper proposed an improved collaborative representation for hyperspectral imagery anomaly detection algorithm. In order to reduce the weights of the anomalous pixels in the background dictionary, using the distance between the atom and the mean of the background dictionary to adjust the weights of the atoms, so as to increase the output of the central pixel in the above conditions. Experimental results show that the proposed algorithm achieves better detection results with different dual windows, and verifies the effectiveness of the proposed algorithm.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-02 Cooperative journals: 《计算机应用研究》
Abstract： The assessment of urban accessibility has always been a hot topic of concern in the research field of smart transportation. The traditional regional accessibility assessment model generally only supports the single dimension data of GIS or GPS as the basic data for the assessment of accessibility, so it is impossible to avoid the problem of inaccurate assessment of regional accessibility due to the influence of external factors. Aiming at this problem, this paper constructs a city area accessibility evaluation model to support multivariate data using the multidimensional data such as GPS vehicle traffic data, time and weather as the basis of regional accessibility. On this basis, the calculation model of the region accessibility ratio based on multidimensional OD matrix is designed in this work which is used as the quantitative methods of regional accessibility to achieve the purpose of improving the accuracy of accessibility assessment. In addition, to solve the problem of traditional GPS data cleaning method, such as effective information missing and inaccurate data correction, which is caused by its over-roughness, the serial data cleaning method based on the statistical theory is applied in this model. The speed and acceleration information of the Taxi GPS data is considered in this data cleaning method to correct the potential error and to improve the GPS data cleaning effect. Experiment result shows that the accuracy of the regional accessibility calculated by using the multivariate data urban area accessibility assessment model proposed in this paper is 9.1% -37.8 higher than that of the traditional methods, and the accuracy of the regional accessibility assessment ratio and travel time are increased by 12.6% -35.5% and 18.5% -31.6% respectively.
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