Online English Resource Integration Algorithm based on high-dimensional Mixed Attribute Data Mining

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2024-04-16 DOI:10.1145/3657289

Zhiyu Zhou

{"title":"Online English Resource Integration Algorithm based on high-dimensional Mixed Attribute Data Mining","authors":"Zhiyu Zhou","doi":"10.1145/3657289","DOIUrl":null,"url":null,"abstract":"<p>To improve the scalability of resources and ensure the effective sharing and utilization of online English resources, an online English resource integration algorithm based on high-dimensional mixed-attribute data mining is proposed. First, an integration structure based on high-dimensional mixed-attribute data mining is constructed. According to this structure, the characteristics of online English resources are extracted, and historical data mining is carried out in combination with the spatial distribution characteristics of resources. In this way, the spatial mapping function of features is established, and the optimal clustering center is designed according to the clustering and fusion structure of online English resources. At this node, the clustering and fusion of online English resources are carried out. According to the fusion results, the distribution structure model of online English resources is constructed, and the optimization research of the integration algorithm of online English resources is carried out. The experimental results show that the integration optimization efficiency of the proposed algorithm is 89%, and the packet loss rate is 0.19%. It has good integration performance, and can realize the integration of multi-channel and various forms of online English resources.</p>","PeriodicalId":54312,"journal":{"name":"ACM Transactions on Asian and Low-Resource Language Information Processing","volume":"38 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Asian and Low-Resource Language Information Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3657289","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

To improve the scalability of resources and ensure the effective sharing and utilization of online English resources, an online English resource integration algorithm based on high-dimensional mixed-attribute data mining is proposed. First, an integration structure based on high-dimensional mixed-attribute data mining is constructed. According to this structure, the characteristics of online English resources are extracted, and historical data mining is carried out in combination with the spatial distribution characteristics of resources. In this way, the spatial mapping function of features is established, and the optimal clustering center is designed according to the clustering and fusion structure of online English resources. At this node, the clustering and fusion of online English resources are carried out. According to the fusion results, the distribution structure model of online English resources is constructed, and the optimization research of the integration algorithm of online English resources is carried out. The experimental results show that the integration optimization efficiency of the proposed algorithm is 89%, and the packet loss rate is 0.19%. It has good integration performance, and can realize the integration of multi-channel and various forms of online English resources.

查看原文本刊更多论文

基于高维混合属性数据挖掘的在线英语资源整合算法

为了提高资源的可扩展性，保证在线英语资源的有效共享和利用，提出了一种基于高维混合属性数据挖掘的在线英语资源整合算法。首先，构建基于高维混合属性数据挖掘的整合结构。根据该结构，提取在线英语资源的特征，结合资源的空间分布特征进行历史数据挖掘。这样就建立了特征的空间映射函数，并根据在线英语资源的聚类融合结构设计出最优聚类中心。在此节点上，进行在线英语资源的聚类与融合。根据融合结果，构建在线英语资源的分布结构模型，开展在线英语资源整合算法的优化研究。实验结果表明，所提算法的整合优化效率为 89%，丢包率为 0.19%。该算法具有良好的整合性能，可以实现多渠道、多种形式的在线英语资源整合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

ACM Transactions on Asian and Low-Resource Language Information Processing Computer Science-General Computer Science

CiteScore

3.60

自引率

15.00%

发文量

241

期刊介绍： The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.