In this research article, we analyze the multimedia data mining and classification algorithm based on database optimization techniques. Of high performance application requirements of various kinds are springing up co...
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In this research article, we analyze the multimedia data mining and classification algorithm based on database optimization techniques. Of high performance application requirements of various kinds are springing up constantly makes parallel computer system structure is valued by more and more common but the corresponding software system development lags far behind the development of the hardware system, it is more obvious in the field of database technology application. Multimedia mining is different from the low level of computer multimedia processing technology and the former focuses on the extracted from huge multimedia collection mode which focused on specific features of understanding or extraction from a single multimedia objects. Our research provides new paradigm for the methodology which will be meaningful and necessary.
A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,t...
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A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,the moving targets are detected by a magnetic sensor and classified with a simple computation method. The detection sensor is used for collecting a disturbance signal of earth magnetic field from an undetermined target. An optimum category match pattern of target signature is tested by training some statistical samples and designing a classification machine. Three ordinary targets are researched in the paper. The experimental results show that the algorithm has a low computation cost and a better sorting accuracy. This classification method can be applied to ground reconnaissance and target intrusion detection.
The current classification is difficult to overcome the high-dimension classification problems. So, we will design the decreasing dimension method. Locally linear embedding is that the local optimum gradually approach...
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The current classification is difficult to overcome the high-dimension classification problems. So, we will design the decreasing dimension method. Locally linear embedding is that the local optimum gradually approaches the global optimum, especially the complicated manifold learning problem used in big data dimensionality reduction needs to find an optimization method to adjust k-nearest neighbors and extract dimensionality. Therefore, we intend to use orthogonal mapping to find the optimization closest neighbors k, and the design is based on the Lebesgue measure constraint processing technology particle swarm locally linear embedding to improve the calculation accuracy of popular learning algorithms. So, we propose classification algorithm based on improved locally linear embedding. The experiment results show that the performance of proposed classification algorithm is best compared with the other algorithm.
The hotel management relationship is a good business strategy for hotels, which can promote the development of a hotel, when a classification algorithm is applied to customer relationship management system. First, the...
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The hotel management relationship is a good business strategy for hotels, which can promote the development of a hotel, when a classification algorithm is applied to customer relationship management system. First, the classification algorithm is based on a support vector machine is studied, the nearest neighbor sample density is used, and the corresponding mathematical model is constructed. Second, the procedure of a classification algorithm based on fuzzy support vector machine is designed. Third, a customer acquisition plan based on a classification algorithm is analyzed. Finally, a hotel is used as the research object, and a customer acquisition analysis is carried out, and the results show that the new method has quicker training speed and higher classification correctness.
Clustering algorithm is one of the most important algorithms in unsupervised learning. For density-based spatial clustering of applications with noise (DBSCAN) density clustering algorithm, the selection of neighborho...
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Clustering algorithm is one of the most important algorithms in unsupervised learning. For density-based spatial clustering of applications with noise (DBSCAN) density clustering algorithm, the selection of neighborhood radius and minimum number is the key to get the best clustering results. Aiming at the problems of traditional DBSCAN algorithm, such as the neighborhood radius and the minimum number of points, this article puts forward two classifications based on K-means algorithm, and gets two clustering centers. Where calculated between two data points and the cluster center-to -center distance, clustering, distance, statistics in a distance of data points within the scope of the search, the number of data points corresponding to the maximum distance value, and thus the parameters for the DBSCAN algorithm to estimate and selection of initial radius of neighborhood with the minimum number of clustering start critical value. When the parameters are iterated and optimized continuously, the data are divided into clusters, and the most suitable neighborhood radius and the minimum point number are obtained. The experimental data analysis show that the improved algorithm reduces the human factors in the traditional algorithm and improves the efficiency, so as to get the accurate clustering results.
Land cover classification is a vital application area in the satellite image processing domain. Texture is a useful feature in land cover classification. In this paper, we propose a distributed texture-based land cove...
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Land cover classification is a vital application area in the satellite image processing domain. Texture is a useful feature in land cover classification. In this paper, we propose a distributed texture-based land cover classification algorithm using Hidden Markov Model (HMM). Here, HMM is used for texture-based classification of remotely sensed images. Furthermore, to enhance the performance, data-intensive remotely sensed image is segmented and distributed into parallel sessions. Experiments were conducted on IRS P6 LISS-IV data, and the results were evaluated based on the confusion matrix, classification accuracy, and Kappa statistics. These results indicate that the proposed algorithm achieves a classification accuracy of 88.75%.
Traditional anomaly detection causes a problem of detecting too numerous false positives in many problem *** this work,a Superimpose Rule-Based classification algorithm(SRBCA) is proposed for conditional anomaly *** a...
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Traditional anomaly detection causes a problem of detecting too numerous false positives in many problem *** this work,a Superimpose Rule-Based classification algorithm(SRBCA) is proposed for conditional anomaly *** algorithm is an enhancement of the traditional OneR *** traditional OneR can generate a set of rules from its attributes with multiple classes,compute the error rate and apply the rule to the attribute with the smallest ***,OneR has a disadvantage for one-class datasets which contains values belonging to the normal *** enhanced algorithm,SRBCA,does not embody very complex rules similar to its ***,SRBCA includes the generation and application of rules from the one-class dataset in an n-dimensional space using *** method was used to evaluate the performance of the classifiers' accuracy which involved training multiple subsets' behavioral and indicator attributes,superimposing rules and testing by using balanced and unbalanced class data to detect and label conditional anomaly data *** paper shows the comparison between SRBCA,One-Class Support Vector Machine(OCSVM) and other anomaly detection classification algorithms for conditional anomaly *** proves that the new method can handle one-class multivariate for conditional anomaly detection with better accuracy.
In this paper, we propose a classification algorithm based on Recency-FrequencyMonetary(RFM) model and K-means data mining method. In addition, the designed algorithm is verified by the experiments on the member data ...
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In this paper, we propose a classification algorithm based on Recency-FrequencyMonetary(RFM) model and K-means data mining method. In addition, the designed algorithm is verified by the experiments on the member data in a large shopping mall. The experiments results show that the proposed algorithm can provide an accurate classification of the ***, some marketing strategies for different classes of members are given according to the classification results.
Various information in the era of Internet big data has shown an "explosive" growth,and mining useful information from text data information is one of natural language processing *** addition to major breakt...
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Various information in the era of Internet big data has shown an "explosive" growth,and mining useful information from text data information is one of natural language processing *** addition to major breakthroughs in image recognition,deep learning convolutional neural networks can also be applied to text *** Chinese data as the research object,a new text classification model is constructed by using the CNN algorithm and the jump-gram combination of convolutional neural *** the same time,the traditional Pinyin classification methods are *** simulation experiments,it is proved that the CNN algorithm has a good effect on text classification,and its classification accuracy is as high as 88%.
Based on the traditional fuzzy BP classification method,the image with high degree of feature phase was classified with higher misclassification *** the problems of the traditional methods,in this paper,a classificati...
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Based on the traditional fuzzy BP classification method,the image with high degree of feature phase was classified with higher misclassification *** the problems of the traditional methods,in this paper,a classification feature similarity image classification algorithm based on deep learning and support vector machine was ***,the local average noise reduction method was used to denoise the similarity image,and the wavelet image was decomposed by wavelet multi-scale decomposition ***,the local information smoothing processing of the image was performed by the RGB color component recombination method,and the rough set feature quantity of the image was ***,the extracted feature quantities were input into a support vector machine learner for image *** the hidden layer of the classifier,adaptive learning of weighting parameters was performed by the deep learning algorithm to achieve image enhancement processing and classification optimization of batch feature similarity *** simulation results showed that the accuracy of feature similarity image classification was better,the ability to resist inter-class attribute perturbation was stronger,and the retrieval efficiency of large-scale images was improved.
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