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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce 1

7th IFIP International Conference on Intelligent Information processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information processing.

关键词： MapReduce

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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

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13th ACIS International Conference on Software Engineering, Artificial intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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Group decision making based on vague preference relations

Group decision making based on vague preference relations

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IEEE International Conference on Granular Computing (GRC)

作者： Jianlong Hu Yanhui Zhai Deyu Li Hua Li Key Laboratory of Computational Intelligence Chinese Information Processing of Ministry of Education Taiyuan China School of Computer and Information Technology Taiyuan China

Vague sets, characterized by a truth-membership function and a false-membership function, was introduced by Gau and Buehrer [1]. In this paper, we define the concepts of vague preference relation and incomplete vague preference relation. Approaches to group decision making based on vague preference relations and based on incomplete vague preference relations respectively are proposed. Then, the vague arithmetic averaging operator and vague weighted arithmetic averaging operator are used to aggregate vague preference information. The ranking method proposed is applied to ranking and selection of alternatives. Finally, by a numerical example, we illustrate the proposed approach.

关键词： Ions

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Feature selection for large-scale data sets in GrC

Feature selection for large-scale data sets in GrC

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IEEE International Conference on Granular Computing (GRC)

作者： Jiye Liang Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Taiyuan China

Granular computing, as an emerging computational and mathematical theory which describes and processes uncertain, vague, incomplete, and mass information, has been successfully used in knowledge discovery. At present, granular computing faces the challenges of consuming a huge amount of computational time and memory space in dealing with largescale and complicated data sets. Feature selection, a common technique for data preprocessing in many areas such as pattern recognition, machine learning and data mining, is of great importance. This paper focuses on efficient feature selection algorithms for large-scale data sets and dynamic data sets in granular computing.

关键词： Educational institutions

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Pulse waveform key point recognition by wavelet transform for central aortic blood pressure estimation

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Journal of Information and computational Science 2012年第1期9卷 25-33页

作者： Zheng, Gang Huang, Qingjun Yan, Guodong Dai, Min Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China Laboratory of Bio Signal and Intelligent Processing Tianjin University of Technology Tianjin 300384 China

Non invasion estimating of Central Aortic blood Pressure (CAP) is still a hard problem. Through tenyears clinical research plan (CAFE, Conduit Artery Functional Endpoint) on CAP, CAP is a vital sign to evaluate human's vascular condition. It is more valuable than ordinary brachial artery blood pressure. Non invasion estimating of CAP is depended on the key point recognition of radial pulse, especially the peak of tidal wave (left ventricular stopping ejection point, point e). Since someone's point e is not obvious in his/her pulse waveform, orientation of this point has becoming a difficult problem to be tackled. Through deep study on the feature of pulse waveform, an modified wavelet transform is proposed. It is used to recognize point e of pulse waveform by relative displacement between the 4th and 5th scale of a symlet (it is a kind of discrete wavelet transform). After experiments on 16 patients and 20 healthy volunteers, the results support the proposed strategy in recognizing both obvious and unobvious point e of pulse waveform. The CAP can be estimated by the point e accurately, and the results can meet the requirement of clinical diagnosis. © 2012 Binary Information Press January 2012.

关键词： Clinical research

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Weakening interaction suppresses spontaneous symmetry breaking in two-channel asymmetric exclusion processes

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Physical Review E 2012年第4期85卷 041132-041132页

作者： Kai-Xuan Zhu Nian Wang Qing-Yi Hao Qi-Yi Liu Rui Jiang Key Laboratory of Intelligent Computing and Signal Processing of Education Ministry Anhui University Hefei 230039 China School of Engineering Science University of Science and Technology of China Hefei 230026 China School of Mathematics and Computational Science Anqing Teachers College Anqing 246133 China

This paper has studied spontaneous symmetry breaking (SSB) phenomenon in two types of two-channel asymmetric simple exclusion processes (ASEPs). One common feature of the two systems is that interactions for each species of particle happen at only one site, and the system reduces to two independent ASEPs when interaction vanishes. It is shown that with the weakening of interaction, the SSB is suppressed. More interestingly, the SSB disappears before the interaction is eliminated. Our work thus indicates that local interaction has to be strong enough to produce SSB. The mean-field analysis has been carried out, and the results are consistent with the simulation ones.

关键词： BROKEN symmetry (Physics) MEAN field theory STOCHASTIC processes SIMULATION methods & models STATISTICAL mechanics SYSTEMS theory

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Sentiment clustering of product object based on feature reduction

Sentiment clustering of product object based on feature redu...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Suge Wang Xueqian Yin Ru Li Jie Zhang Yunyun Lv School of Computer and Information Technology Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan China School of Mathematics Science Shanxi University Taiyuan China School of Computer and Information Technology Shanxi University Taiyuan China

Face to car domain, the content of reviews is very scattered. In order to evaluate rate of each product, we extract the sentences from reviews. Then, those sentences are merged which describe the same product together and summarizing them according to product performances. On this basis, we extract features from these sentences, and calculate their value. Owing to existing missing feature values, established information systems are called incomplete information systems. For the problems of high dimension and missing data, we adopt the feature reduction algorithm based on discernibility matrix to reduce the feature dimension. Lastly, we aggregate each product by K-means clustering algorithm. Our experimental results indicate that the proposed method is effective.

关键词： Feature extraction Information systems Clustering algorithms Educational institutions Algorithm design and analysis Semantics Ontologies

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Editorial

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International Journal on Artificial intelligence Tools 2012年第3期21卷

作者： Cui, Zhihua Shi, Zhongzhi Alex, Rajan Complex System and Computational Intelligence Laboratory School of Computer Science and Technology Taiyuan University of Science and Technology Shanxi 030024 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuan South Road Zhongguancun Haidian District Beijing 100190 China Department of Engineering and Computer Science West Texas AandM University Canyon TX 79015 United States

Swarm intelligence is an umbrella for amount optimization algorithms. This discipline deals with natural and artificial systems composed of many individuals that coordinate their activities using decentralized control and self-organization. In general, multi-agent systems that use some swarm intelligence are said to be swarm intelligent systems. They are mostly used as search engines and optimization tools. The goal of this special issue has been to offer a wide spectrum of sample works throughout the world about innovative methodologies of swarm intelligence. The issue should be useful both for beginners and experienced researchers in the field of computational intelligence. © 2012 World Scientific Publishing Company.

关键词： Swarm intelligence

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基于数据场的改进DBSCAN聚类算法

基于数据场的改进DBSCAN聚类算法

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第29届中国数据库学术会议

作者： YANG Jing 杨静 GAO Jiawei 高嘉伟 LIANG Jiye 梁吉业 LIU Yanglei 刘杨磊 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Educa 山西大学计算智能与中文信息处理教育部重点实验室太原030006 山西大学计算机与信息技术学院太原030006 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Educa 山西大学计算智能与中文信息处理教育部重点实验室太原030006 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Educa

DBSCAN算法是一种典型的基于密度的聚类算法。该算法可以识别任意形状的类簇，但聚类结果依赖于参数Eps和MinPts的选择，而且对于一些密度差别较大的数据集，可能得不到具有正确类簇个数的聚类结果，也可能将部分数据错分为噪声。为此... 详细信息

DBSCAN算法是一种典型的基于密度的聚类算法。该算法可以识别任意形状的类簇，但聚类结果依赖于参数Eps和MinPts的选择，而且对于一些密度差别较大的数据集，可能得不到具有正确类簇个数的聚类结果，也可能将部分数据错分为噪声。为此，利用数据场能较好描述数据分布，反映数据关系的优势，提出了一种基于数据场的改进DBSCAN聚类算法。该算法引入平均势差的概念，在聚类过程中动态地确定每个类的Eps和平均势差，从而能够在一些密度相差较大的数据集上得到较好的聚类结果。实验表明，所提算法的性能优于DBSCAN算法。

关键词：数据场聚类算法全局优化性能测试

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Information theory related learning 19

Information theory related learning

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19th European Symposium on Artificial Neural Networks, ESANN 2011

作者： Villmann, T. Cichocki, A. Principe, J. University of Applied Sciences Mittweida Dep. of Mathematics/Natural and Computer Sciences Computational Intelligence Group Technikumplatz 17 Mittweida09648 Germany Riken Brain Sience Institute Lab Advanced Brain Signal Processing 2-1 Hirosawa Wako City Saitama351-0198 Japan University of Florida Computational NeuroEngineering Laboratory GainesvilleFL32611 - United States

ISBN: (纸本)9782874190445

This is the introduction paper to a special session held on ESANN conference 2011. It reviews and highlights recent developments and new direction in information related learning, which is a fastly developing research area. These algorithms are based on the fundamental principles of information theory and relate them implicitly or explicitly to learning algoithms and strategies. © European Symposium on Artificial Neural Networks. All rights reserved.

关键词： Information theory

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