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共 916 条记录，以下是181-190 订阅

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Improving Matrix Factorization Recommendations for Problems in Big data 2

Improving Matrix Factorization Recommendations for Problems ...

引用

2017 IEEE 2nd international conference on Big data Analysis（ICBDA 2017）

作者： Xiaohan Tu Siping Liu Renfa Li College of Computer Science and Electronic Engineering Hunan University

ISBN: (纸本)9781509036202

In the big data environment,recommender systems are facing many problems,such as poor extendibility,data sparseness and low *** this paper,a new collaborative filtering parallel algorithm named NALS-WR is designed in the Linux clusters by using Spark to solve these problems,especially aiming at the bottleneck of processing speed and resource allocation of traditional matrix factorization algorithm in massive data *** experiments were on real Movie Lens *** with other such recommendation systems based on ALS-WR or sigular value decomposition(SVD),our accuracy rate was *** running efficiency was much higher than ALSWR in Hadoop and *** bigger the scale of data,the more efficient it *** to the distributed computing framework and cloud computing environment,our model is more sufficiently ***-WR can improve the execution efficiency of collaborative filtering recommendation algorithm at large data *** experiments also indicated that NALS-WR algorithm was better,whether in extendibility,sparseness resistance or efficiency in the implementation.

关键词： component recommender systems data sparsity problem collaborative filtering alternating-least-squares with Weighted-λ-regularization(ALS-WR)

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Research on High-Risk Student Prediction Based on Big data 2

Research on High-Risk Student Prediction Based on Big Data

引用

2017 IEEE 2nd international conference on Big data Analysis（ICBDA 2017）

作者： Yu Xiaogao Peng Ruiqing School of information management and statistics Hubei University of Economics

ISBN: (纸本)9781509036202

In recent years,the development of smart phones,Internet of things and cloud computing provides massive data for the study of education big ***,the current research on education big data is not enough,as the data is small,the processing method is simple and the scope of application is *** this paper,big data technology is adopted and the massive data of multiple data sources are utilized,the model features are extracted by well-designed data processing methods,and a more general high-risk student prediction model is *** research of this paper can broaden the research methods and application scope of education big data,so it has strong theoretical value and important practical value.

关键词： high-risk prediction big data model

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Diagnostic Analysis of Breast Cancer in Non-Cancer History Patients Using Fuzzy Mapreduce Framework 2

Diagnostic Analysis of Breast Cancer in Non-Cancer History P...

引用

2nd international conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2017

作者： Vaishnavi, K.R. Bhargavi, K. Saisupriya, Vadla Vidya, K. Soumya, S. Department of Computer Science and Engineering Siddaganga Institute of Technology Tumkur Karnataka572103 India

ISBN: (纸本)9781538620441

Breast cancer is most commonly occurring cancer among women of all ages and surprisingly in recent days it is found that women with no cancer history are being diagnosed with breast cancer. A lot of breast cancer related data is available on the web but it involves uncertainty. So, there is a need to use noise proof computing techniques to draw proper inferences about the disease. In this paper, fuzzy soft computing approach combined with the MapReduce framework is used to handle noisy data about cancer on web in order to precisely recognise the occurrence of breast cancer in non-cancer history patients at early stages. © 2017 IEEE.

关键词： MapReduce

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Propagation Characteristics of Micro-blog “Big V” Users' Public Opinions in Social Breaking News as Big data 2

Propagation Characteristics of Micro-blog “Big V” Users' P...

引用

2017 IEEE 2nd international conference on Big data Analysis（ICBDA 2017）

作者： Yanling Sun Mingchun Zheng Shandong Normal University

ISBN: (纸本)9781509036202

With the development of Sina Weibo,the amount of user keeps ***-blog "big V" users play an important role in the dissemination process of micro *** emergencies appeared constantly and spread on Sina Weibo,sometimes with rumors,so it is necessary to discover micro-blog "big V" users' public opinion propagation characteristics in order to set a health network *** paper gathered the data from Sina Weibo about "Beijing Heyi Hotel".Through the micro-blogging visualization and social network analysis,we found that the small world phenomenon between Micro-blog "big V" users is more obvious;Betweenness centrality of weibo users is consistent with the "long-tail" theory;There exists a new style of "two-step" communication--Self-propagation *** paper studied the spreading of public opinion of micro-blog "big V" users,thus can help the government strengthen the control and supervision of online public opinion.

关键词： social breaking news micro-blog "big V" users public opinion propagation Beijing Heyi Hotel

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Zipf's Law in MOOC Learning Behavior 2

Zipf's Law in MOOC Learning Behavior

引用

2017 IEEE 2nd international conference on Big data Analysis（ICBDA 2017）

作者： Chang Men Xiu Li Zhihui Du Jason Liu Manli Li Xiaolei Zhang Graduate School at Shenzhen Tsinghua University Department of Computer Science and Technology Tsinghua University School of Computing and Information Sciences Florida International University Institute of Education Tsinghua University

ISBN: (纸本)9781509036202

Learners participating in Massive Open Online Courses(MOOC) have a wide range of backgrounds and *** MOOC learners sign up the courses to take a brief look;only a few go through the entire content,and even fewer are able to eventually obtain a *** discovered this phenomenon after having examined 76 courses on the xuetang X *** specifically,we found that in many courses the learning coverage—one of the metrics used to estimate the learners' active engagement with the online courses—observes a Zipf *** apply the maximum likelihood estimation method to fit the Zipf's law and test our hypothesis using a chi-square *** result from our study is expected to bring insight to the unique learning behavior on MOOC and thus help improve the effectiveness of MOOC learning platforms and the design of courses.

关键词： MOOC learning coverage maximum likelihood estimation Zipf distribution

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Efficient Fast Updated Frequent Pattern tree algorithm and its parallel implementation 2

Efficient Fast Updated Frequent Pattern tree algorithm and i...

引用

2nd international conference on Image, Vision and computing, ICIVC 2017

作者： Lv, Detao Fu, Bo Sun, Xiao Qiu, Hang Liu, Xiaobing Yanlong, Zhang Department of Computer Science and Engineering UESTC Chengdu China Big Data Research Center UESTC Chengdu China Shulianyikang Science and Technology Co. LTD Chengdu China

ISBN: (纸本)9781509062379

With the rapid expansion of data, incremental update mining for associated rule learning in large scale volume of data processing is concerned. The support count or support ratio are often used as a threshold for the associated rule learning. The support ratio is sensitive to the threshold, especially, adapted to build anomaly detection models learned with unbalanced samples. It might result in missing anomalies or data failures. However, the support count with the characteristic of good explanatory is easy to use. Therefore, the support ratio always needs to be converted into the support count. In order to mine large-scale frequent patterns, based on the FUFP algorithm, an efficient algorithm of incremental frequent item mining is proposed in this paper, named EFUFP (Efficient Fast Updated Frequent Pattern) tree algorithm. The minimum support count and a small item cache are used to effectively detect unbalanced abnormal data and improve the efficiency of query access. The parallelization of EFUFP is implemented on the Spark environment. The experimental results show that the EFUFP algorithm is suitable for the current large scale frequent item mining. © 2017 IEEE.

关键词： Trees (mathematics)

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An Event Timeline Extraction Method Based on News Corpus 2

An Event Timeline Extraction Method Based on News Corpus

引用

2017 IEEE 2nd international conference on Big data Analysis（ICBDA 2017）

作者： Yaguang Wu Haichun Sun Chungang Yan Department of Computer Science and Technology Tongji University The Key Laboratory of Embedded System and Service Computing Tongji University College of Police Information Engineering People's Public Security University of China

ISBN: (纸本)9781509036202

Event extraction is an important research point in information extraction area, and news event extraction has a greater practical significance. The existing methods of extracting news event, which starts from the time element, is to identify the date sentences hold by Natural Language Processing and extract the event on the date by Text Clustering. However, they only process the news that holds a time-tag and gives up the news that has no accurate date, which easily leads an extraction deviation of significant events and reduces the accuracy of ordering the significant events. In this paper, improvement for this defect is to calculate the similarities between sentences to put part of the non-time-tag sentences into the right date container, thereby the accuracy of ordering the significant event is *** experiments and compared with the existing methods,the accuracy of ordering the significant events in this paper is improved by 14.6%.

关键词： event extraction text information mining sentence similarity timeline

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Research on the Architecture of Internet of Things and Related Technologies

Research on the Architecture of Internet of Things and Relat...

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2018 2nd international conference on Systems,computing,and Applications(SYSTCA 2018)

作者： Xia Wei Xi'an International University

IOT is essentially a physical structure, based on the network, and IOT is undoubtedly an important product of the era of social science and technology. IOT technology has a(http://***/) widespread concern, according to the relevant data that Google search volume of "IOT" in 2016 is up to 100 million, showing that the attention, so IOT is most popular. It is necessary to carry out an in-depth study and analysis of IOT under the background of this kind of environment. This paper analyzes the background and connotation of IOT, and then studies the IOT architecture. At last, this paper will elaborate the actual promotion mode of IOT technology level, with a view to providing theoretical basis for the follow-up study on IOT.

关键词： Internet of Things Architecture Technology

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Application of Anomaly Detection for Detecting Anomalous Records of Terroris Attacks 2

Application of Anomaly Detection for Detecting Anomalous Rec...

引用

2nd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Meng, Xi Zhao, Shenhe Mo, Haowen Li, Jianqiang Peoples Publ Secur Univ China Sch Crime Invest & Counter Terrorism Beijing Peoples R China Beijing Univ Technol Sch Soft Engn Beijing Peoples R China

ISBN: (纸本)9781509044993

Nowadays, terrorism has evolved into such a destructive threat to the whole world that it is calling for an increasing devotion of professional researches and explorations. Machine learning, as a powerful weapon to unveil the hidden knowledge, has been successfully applied into the anti-terrorism field. The aim of this paper is as follows: by implementing anomaly detection algorithm into a famous terrorism database-GTD, we aim to locate the anomalous records within and in doing so, we attempt to present a list of outliers which deviate from the rest of the data. These finding anomalous observations could carry great hidden information which is interesting from a terrorism researcher's perspective. Then empirical analysis and experimental evidence are provided to support the reliability and effectiveness of the outcome. We present some examples from the anomaly list and elaborate their abnormality. Besides, we also validate the irregularity of these finding anomalies from the respect of an improved classification precision, since these exceptions could be incurred by some human errors which turn them into noises and by removing these noise-like objects, we can achieve a higher classification precision. The classification section is extended with three sophisticated classifiers, Support Vector Machine (SVM), Naive Bayes (NB) and Logistic Regression (LR).

关键词： anomaly detection terrorism Global Terrorism database (GTD) classification

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Exploration on College Education Big data Open Service Platform 2

Exploration on College Education Big Data Open Service Platf...

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2nd IEEE international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Shu, Jiangbo Wang, Xu Wang, Li Zhang, Zhaoli Liu, Rai Ru, Qianqian Zhi, Min Cent China Normal Univ Natl Engn Res Ctr E Learning Wuhan Hubei Peoples R China

ISBN: (纸本)9781509044993

With the gradual improvement of the level of educational information, the idea of education big data has gone deep into people's minds. More and more teaching staff and researchers have the consciousness of the data needs, and expect to do the data mining and learning analysis of education big data. However, the most realistic and basic problem they have in the process of carrying out research is that there is no good data support or a convenient way to get data. Aiming at the problem, this article puts forward an education big data open service model, and builds the education big data open service platform based on the model. The platform has tried to run on the Central China Normal University and provides some data service for all the teachers and students in school. The data acquisition trouble is solved perfectly for teaching staff and researchers.

关键词： education big data open service model

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