This study aims to explore how to utilize ASP NET technology and k-means clustering algorithm are used to analyze the humanistic quality evaluation data of private university students. Firstly, by introducing ASP We h...
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ISBN:
(纸本)9798350375343;9798350375336
This study aims to explore how to utilize ASP NET technology and k-means clustering algorithm are used to analyze the humanistic quality evaluation data of private university students. Firstly, by introducing ASP We have explained the basic principles and functions of NET, and its application in efficiently processing and managing large amounts of educational data. Next, the working principle of k-means algorithm and its effectiveness in data clustering were introduced in detail. In the study, we constructed a humanistic quality evaluation system that includes multiple evaluation indicators, aiming to comprehensively evaluate the humanistic quality of students. By collecting relevant data on students from private universities and utilizing ASP NET is used for data preprocessing and management, and then k-means algorithm is applied for data clustering analysis. The results revealed the distribution of different dimensions of humanistic literacy and identified key influencing factors, providing data support for further improving education quality and student humanistic literacy. In addition, this study also discussed the limitations of the method and made suggestions for future research directions. Through this study, we demonstrated ASP The practicality of NET technology and k-means algorithm in educational data analysis provides a new perspective for private universities to evaluate and enhance students' humanistic qualities.
Traditional k-means algorithm randomly select initial cluster center and the quality of clustering results depends on the selection of initial cluster center. If isolate points are selected, the algorithm iterations w...
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In this paper an improved k-means algorithm is presented to cut character out of the license plate images. Although there are many existing commercial LPR systems, with poor illumination conditions and moving vehicle ...
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ISBN:
(纸本)9783642240874;9783642240881
In this paper an improved k-means algorithm is presented to cut character out of the license plate images. Although there are many existing commercial LPR systems, with poor illumination conditions and moving vehicle the accuracy impaired. After examination and comparison of different image segmentation approaches, the k-means algorithm based method gave better image segmentation results. The k-means algorithm was modified by introducing automatic cluster number determination by filtering SIFT key points. After modification it efficiently detects the local maxima that represent different clusters in the image. The process is successful by getting a clean license plate image. While testing by the OCR software, the experimental results show a high accuracy of image segmentation and significantly higher recognition rate. The recognition rate increased from about 86.6% before our proposed process to about 94.03% after all unwanted non-character areas are removed. Hence, the overall recognition accuracy of LPR was improved.
Development of highway transportation promotes sustainable and rapid development in economy of our country effectively. But construction of highway and transportation hub shows the nature of unbalance. So highway main...
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ISBN:
(纸本)9781424413850
Development of highway transportation promotes sustainable and rapid development in economy of our country effectively. But construction of highway and transportation hub shows the nature of unbalance. So highway main hub cities must be clustered using cluster analysis, and then divided level in order to functional analyze. k-means algorithm is the most widely rued algorithm in clustering analysis, which clustering numbers and initial clustering centers are uncertain. This paper proposes application of k-means algorithm in macroscopic planning of highway transportation hub based on ant clustering algorithm. The experimental results show this algorithm can more effectively solved clustering problem than k-means algorithm and ant clustering algorithm.
Unlike the traditional customer relationship management, e-commerce can not only retain transaction data, but also collect other customer information, such as browsing data, comments, and preferences. In order to solv...
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ISBN:
(纸本)9781538645093
Unlike the traditional customer relationship management, e-commerce can not only retain transaction data, but also collect other customer information, such as browsing data, comments, and preferences. In order to solve the problem of B2C e-commerce customer classification, k-means algorithm was selected as the clustering classification method, and an improved RFM value model was proposed to add indicators that meet the e-commerce domain characteristics. The customer group in the B2C environment is well classified.
This paper proposed a new method for unmanned aerial vehicle (UAV) path planning based on k-means algorithm and simulated annealing (SA) algorithm, which solves the problem for multi-UAVs with multi-mission under comp...
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ISBN:
(纸本)9789881563958
This paper proposed a new method for unmanned aerial vehicle (UAV) path planning based on k-means algorithm and simulated annealing (SA) algorithm, which solves the problem for multi-UAVs with multi-mission under complicated constraints. Firstly, the model is established for the no-fly zone, the target zone and the valid zone for cruise within it in the mission area. Then, the decomposition technique decomposes the valid area into multiple sub-target points reasonably. Secondly, the k-means algorithm is used to cluster the target points of UAV cruise, which solves the problem for UAV cruise range and scheduling issues. Combining the SA algorithm for the similar sub-target route planning, this technique increases the coverage of the UAVs in the sub-target area of cruise valid area. Finally, taking the real data of UAVs in earthquake relief as an example, the effectiveness and robustness of the proposed method is validated by simulation experiments.
In order to optimize the effectiveness and efficiency of software test cases, this paper proposed an improved k-means algorithm for test case optimization, introduced Degree of Membership Function to improve k-means a...
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ISBN:
(纸本)9781728113227
In order to optimize the effectiveness and efficiency of software test cases, this paper proposed an improved k-means algorithm for test case optimization, introduced Degree of Membership Function to improve k-means algorithm to design a fuzzy clustering method, and combined the test requirements set, extracted test cases from each cluster, found similar test cases as more as possible. Experimental results showed that this algorithm can minimize the redundant test case set, keep the widest coverage at the same time, and has higher effectiveness and efficiency.
There exist many customers in credit market that needs to be classified into distinct groups. k-means algorithm are presented, which based on the historical financial ratios, utilizing the cluster analysis technology ...
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ISBN:
(纸本)1424403170
There exist many customers in credit market that needs to be classified into distinct groups. k-means algorithm are presented, which based on the historical financial ratios, utilizing the cluster analysis technology to analyze the listed enterprises in Zhejiang province. Some indicators related to financial attributes are analyzed, and nine finance indicators are chosen. According to better valuation on the companies listed, we apply to "try and error" and choose 4 as the number of clustering. 81 samples are divided into two groups :one training group with 60 firms and other testing group with 21 *** results shows that the model trained can be available for clustering companies listed in Zhejiang province.
This study aims to introduce a design method of Jaccard Distance Coefficient with k-means algorithm for machine component clustering into independent modules so that a machine can be easily modified to achieve its req...
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ISBN:
(纸本)9781728167855
This study aims to introduce a design method of Jaccard Distance Coefficient with k-means algorithm for machine component clustering into independent modules so that a machine can be easily modified to achieve its requirement functions. In this study, Jaccard Distance Coefficient is firstly applied to generate relation matrices. After that, six results of distance coefficients and their clusters (2.02 with 2 clusters, 1.95 with 3 clusters, 1.89 with 4 clusters, 1.83 with 5 clusters, 1.75 with 6 clusters and 1.66 with 9 clusters) are calculated corresponding to relation matrices from the first step. Nextly, k-means algorithm is used to take care of these six results for analyzing the most proper level coefficient. The result shows that the modules of 3 clusters with distance coefficient of 1.95 is the best outcome at the 0.7074 natural value.
In k-means clustering algorithm, the selection of cluster number k and initial k-means center has certain influence on the result. It would generate very different aggregation result when confronting with some certain...
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ISBN:
(纸本)9781467365932
In k-means clustering algorithm, the selection of cluster number k and initial k-means center has certain influence on the result. It would generate very different aggregation result when confronting with some certain types of data set. This paper aims at proposing an estimation method to evaluate the initial parameters for k-means algorithm. The estimation is executed through data analysis, which contains two main steps: the data would be transformed into data dimensional density first, and then, watershed method would be applied to divide the data space into multiple regions. Each regional center is selected as an initial k-means center, and the number of region is set as cluster number. This estimation method takes advantage of image segmentation ideology and the case study in this paper showed its favorable performance.
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