In the era of big data, people's visual needs for data expression are increasing. In order to achieve better big data display effects, this article introduced the use of text clustering algorithms to achieve data ...
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ISBN:
(纸本)9781450388597
In the era of big data, people's visual needs for data expression are increasing. In order to achieve better big data display effects, this article introduced the use of text clustering algorithms to achieve data crawling and Echarts technology to realize big data visualization. This system used mvvm's architecture and vue framework development platform, ThinkPHP was used as the background framework, and ES6 related technologies and specifications were used for application development. This system used Echarts, IView, GIS technology and JavaScript development methods to demonstrate economic big data module functions on the web side; Applied CSS3, HTML5, GIS technology to implement project achievement module and university alliance module; Applied Echarts, HTML5, JS function library technology to achieve national information module. This system used stored procedure, database index optimization technology to achieve rapid screening of massive data, and dynamically update and displayed related data through two-way data binding. This system combined real-time location technology with GIS technology to measure the distance between the user and the destination, and automatically plan the tour route to provide related services. This system can provide feasibility suggestions for strategic researchers or experts in related areas of the “Belt and Road”, and provide theoretical basis and technical support.
textclustering is an important method for effectively organising, summarising, and navigating text information. However, in the absence of labels, the text data to be clustered cannot be used to train the text repres...
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textclustering is an important method for effectively organising, summarising, and navigating text information. However, in the absence of labels, the text data to be clustered cannot be used to train the text representation model based on deep learning. To address the problem, an algorithm of textclustering based on deep representation learning is proposed using the transfer learning domain adaptation and the parameters update during cluster iteration. First, source domain data is used to perform the pre-training of the deep learning classification model. This procedure acts as an initialisation of the model parameters. Then, the domain discriminator is added to the model, to domain-divide the input sample. If the discriminator cannot distinguish which domain the data belongs to, the common feature space of two domains is obtained, so the domain adaptation problem is solved. Finally, the text feature vectors obtained by the model are clustered with MCSKM++ algorithm. The algorithm not only resolves the model pre-training problem in unsupervised clustering, but also has a good clustering effect on the transfer problem caused by different numbers of domain labels. Experiments suggest that the clustering accuracy of the algorithm is superior to other similar algorithms.
With the popularity of Internet and large-scale improvement in the level of enterprise information, the explosive growth of resources, the research of text mining, information filtering and information search appe...
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With the popularity of Internet and large-scale improvement in the level of enterprise information, the explosive growth of resources, the research of text mining, information filtering and information search appear the unprecedented prospect. So, the cluster technology is becoming the core of text information mining technologies. clustering is an important form of data mining. This paper introduces common text clustering algorithms, analyses and compares some aspects of clusteringalgorithms which contains the applicable scope, the initial parameters, termination conditions and noise sensitivity. algorithms contain hierarchical clustering, partitioned clustering, density-based algorithm and self-organizing maps algorithm.
With the rapid development of Internet, more and more massive information, search engine technology developed rapidly, but the search engine's search results don't not meet the search requirements. The k-means...
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ISBN:
(纸本)9783642242724
With the rapid development of Internet, more and more massive information, search engine technology developed rapidly, but the search engine's search results don't not meet the search requirements. The k-means clusteringalgorithm are introduced to gather web documents class, in order to improve the clustering performance, the introduction of leapfrog algorithm selection of k value aiming to improve the accuracy of search results and to increase the search engine returns results associated with the query topic.
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