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检索条件"主题词=TFIDF algorithm"

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Research and Improvement of feature words weight based on tfidf algorithm

Research and Improvement of feature words weight based on TF...

引用

IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)

作者： Guo, Aizhang Yang, Tao Qilu Univ Technol Jinan 250353 Peoples R China

ISBN: (纸本)9781467391948

With the development of cloud era, more and more people have been attracted by Big data. More and more applications involve large data. Analysis methods of large data is particularly important. This paper mainly analyzes and research feature words weight which are used in unstructured data classification of big data. Firstly, we combine the traditional feature words weight calculation method and analyze the shortcoming of traditional TF-IDF algorithm, It doesn't think about feature words distribution. It can lead that some feature words weight which don't have strong discrimination have heavier weight. Aiming at the shortage of tfidf algorithm, combining with practical effect to text classification, this paper modify traditional tfidf algorithm formula, excluding the inner impact to disturb characteristic, adding the concept of intra-class dispersion, presenting a new tfidf algorithm. In the experiment, experimental data comes from People news about the financial, military, entertainment and sports four categories, respectively calculating test value by using the traditional tfidf algorithm and improved tfidf algorithm. Results show that improved tfidf algorithm has higher accuracy than traditional tfidf algorithms.

关键词： tfidf algorithm Text classification Feature selection Feature weighting

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Research and Improvement of feature words weight based on tfidf algorithm

Research and Improvement of feature words weight based on TF...

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2016 IEEE Information Technology,Networking,Electronic and Automation Control Conference(ITNEC 2016)

作者： Aizhang Guo Tao Yang Qilu University of Technology

ISBN: (纸本)9781467391955

关键词： tfidf algorithm Text classification Feature selection Feature weighting

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Improving Government-Data Learning via Distributed Clustering Analysis 7

Improving Government-Data Learning via Distributed Clusterin...

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7th International Conference on Cloud Computing and Big Data (CCBD)

作者： Zhong, Yurong Univ Elect Sci Technol China Res Inst Elect Sci & Technol Chengdu Sichuan Peoples R China

ISBN: (纸本)9781509035557

Clustering analysis is a study which is of great value, and the large-scale government-data needed to be handled by cluster analysis is growing increasingly. Efficient analysis techniques of large-scale data need to be adopted to handle the large-scale data. Traditional model of serial programming has serious scalability shortage, which don't satisfy the need of the large-scale government-data handling for computing and storage resources. Distributed computing technology represented by the MapReduce has good scalability, and can greatly improve the execution efficiency of data-intensive algorithm, and give play to the computing power of compute cluster based on general hardware. Based on the background of "data platform for public petition", it aims to study how to combine the cluster analysis technology with the current massive government-data, extracting useful information from the mass characteristics hidden in the data through the cluster analysis technology, which can provide comprehensive analyse for system managers and decision makers. This paper focus on the study of combining basic distributed clustering algorithm and TF-IDF algorithm, developing the cases feature analysis module based on distributed clustering algorithm. Based on distributed clustering algorithm, according to the information of the cases, do clustering analysis of cases according to its characteristics, and then get several hidden information through serveral decisional result.

关键词： component government-data distributed clustering algorithm tfidf algorithm parallel computing feature selection analysis of cases

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