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IEEE Symposium on Computational Intelligence and data Mining

作者： Li, Pei Li, Zhixu He, Jun Du, Xiaoyong Liu, Hongyan Key Labs of Data Engineering and Knowledge Engineering MOE China and School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9781424427659

Influence between objects needs to be assessed in many applications. Lots of measures have been proposed, but a domain-independent method is still expected. In this paper, we give a probabilistic definition of influence based on the random walker model on graphs. Two approaches, linear systems method and Basic InfRank algorithm, are shown and return equal results, but Basic InfRank is more efficient by iterative computation. Two variants on bipartite graphs and star graphs are discussed. Experiments show InfRank algorithms have good accuracy, fast convergent rate and high performance.

关键词： Linear systems

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Median query research in wireless sensor networks

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2010年第2A期38卷 133-137页

作者： Wu, Zhong-Bo Zhang, Hui Chen, Hong Information School Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China School of Mathematics and Computer Science Xiangfan University Xiangfan Hubei 441053 China

Poor quality and harsh condition can result in faulty and outlier data in sampling data of sensor nodes. So we need median query to reflect average level of monitoring region. First, we put forward HMA algorithm. Second, we extend HMA algorithm and put forward HFMA algorithm. In HFMA, We only need collect data inside filter and aggregate influence coefficient during sampling period. Base station can compute median result according to the sample data inside filter and influence coefficient aggregation value. Experimental results have shown that HFMA outperforms Naive algorithm and HMA algorithm and can prolong the lifetime of sensor network.

关键词： Sensor nodes

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Approximation algorithm for constructing data aggregation trees for wireless sensor networks

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中国高等学校学术文摘·计算机科学 2009年第4期3卷 524-534页

作者： Deying LI Jiannong CAO Qinghua ZHU Key Laboratory of Data Engineering and Knowledge Engineering School of InformationRenmin University of ChinaBeijing 100872China Internet and Mobile Computing Lab Department of ComputingHong Kong Polytechnic UniversityHong KongChina

This paper considers the problem of constructing data aggregation trees in wireless sensor networks (WSNs)for a group of sensor nodes to send collected information to a single sink *** data aggregation tree contains the sink node,all the source nodes,and some other non-source *** goal of constructing such a data aggregation tree is to minimize the number of non-source nodes to be included in the tree so as to save *** prove that the data aggregation tree problem is NP-hard and then propose an approximation algorithm with a performance ratio of four and a greedy *** also give a distributed version of the approximation *** simulations are performed to study the performance of the proposed *** results show that the proposed algorithms can find a tree of a good approximation to the optimal tree and has a high degree of scalability.

关键词： WSNs data aggregation tree approximation algorithm distributed algorithm

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Using link-based content analysis to measure document similarity effectively

Using link-based content analysis to measure document simila...

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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Li, Pei Li, Zhixu Liu, Hongyan He, Jun Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9783642006715

Along with a massive amount of information being placed online, it is a challenge to exploit the internal and external information of documents when assessing similarity between them. A variety of approaches have been proposed to model the document similarity based on different foundations, but usually they are not applicable for combining internal and external information. In this paper, we introduce a link-based method into content analysis, which is based on random walk on graphs. By defining similarity as the meeting probability of two random surfers, we propose a computational model for content analysis, which can also be integrated with external information of documents. Empirical study shows that our method achieves good accuracy, acceptable performance and fast convergent rate in multi-relational document similarity measuring. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Computers

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Exploiting the block structure of link graph for efficient similarity computation

Exploiting the block structure of link graph for efficient s...

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13th Pacific-Asia Conference on knowledge Discovery and data Mining, PAKDD 2009

作者： Pei, Li Yuanzhe, Cai Hongyan, Liu Jun, He Xiaoyong, Du Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)3642013066

In many real-world domains, link graph is one of the most effective ways to model the relationships between objects. Measuring the similarity of objects in a link graph is studied by many researchers, but an effective and efficient method is still expected. Based on our observation of link graphs from real domains, we find the block structure naturally exists. We propose an algorithm called BlockSimRank, which partitions the link graph into blocks, and obtains similarity of each node-pair in the graph efficiently. Our method is based on random walk on two-layer model, with time complexity as low as O(n4/3) and less memory need. Experiments show that the accuracy of BlockSimRank is acceptable when the time cost is the lowest. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Artificial intelligence

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MrCAR: A Multi-relational Classification Algorithm based on Association Rules

MrCAR: A Multi-relational Classification Algorithm based on ...

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International Conference on Web Information Systems and Mining

作者： Gu, Yingqin Liu, Hongyan He, Jun Hu, Bo Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering MOE China Information School Renmin University of China Beijing 100872 China School of Economics and Management Tsinghua University Beijing 100084 China

ISBN: (纸本)9780769538174

Classification is an important subject in data mining and machine learning, which has been studied extensively and has a wide range of applications. Classification based on association rules is one of the most effective classification method, whose accuracy is higher and discovered rules are easier to understand comparing with classical classification methods. However, current algorithms for classification based on association rules is single table oriented, which means they can only apply to the data stored in a single relational table. Directly applying these algorithms in multi-relational data environment will result in many problems. This paper proposes a novel algorithm MrCAR for classification based on association rules in multi-relational data environment. MrCAR mines relevant features in each table to predict the class label. Close itemsets technique and Tuple ID Propagation method are used to improve the performance of the algorithm. Experimental results show that MrCAR has higher accuracy and better understandability comparing with a typical existing multirelational classification algorithm.

关键词： Association rules

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AutoPCS: A phrase-based text categorization system for similar texts

AutoPCS: A phrase-based text categorization system for simil...

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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Li, Zhixu Li, Pei Wei, Wei Liu, Hongyan He, Jun Liu, Tao Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9783642006715

Nearly all text classification methods classify texts into predefined categories according to the terms appeared in texts. State-of-the-art of text classification prefer to simplely take a word as a term since it performs good on some famous datasets;some experts even pointed out that phrases don't improve or improve only marginally the classifiction accuracy. However, we found out that this is not always true when we try to categorize texts about similar topics in the same domain. With words only we can not categorize those texts effectively since they nearly share the same word set. Then we suppose the results might be improved if we also use phrases as terms. To testify our supposition, we propose our own phrase extraction way as well as select proper feature selection method and classifier by conducting experimental study on a data set which comes from paper abstracts in the field of databases. Accordingly, we also develop a system called AutoPCS which can be used to help experts in choosing relevant topics for newly coming papers from a predefined topic list only by their abstracts. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Classification (of information)

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Deprank: A probabilistic measure of dependence via heterogeneous links

Deprank: A probabilistic measure of dependence via heterogen...

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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Li, Pei Hu, Bo Liu, Hongyan He, Jun Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9783642006715

Dependence is a common relationship between objects. Many works have paid their attentions on dependence, but many of them mainly focus on constructing or exploiting dependence graphs on some specific domain. In this paper, we give a generic definition of dependence and make equivalence to the situation of graphs, and propose an algorithm called DepRank, which quantifies the dependence degree of a node pair based on probabilities of all dependence paths. Empirical study shows that DepRank method has reasonable results and maintains a well balance between accuracy and efficiency. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Artificial intelligence

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Two Novel Semantics of Top-k Queries Processing in Uncertain database

Two Novel Semantics of Top-k Queries Processing in Uncertain...

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International Conference on Computer and Information Technology (CIT)

作者： Dexi Liu Changxuan Wan N. Xiong Laurence T. Yang Lei Chen School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China Department of Computer Science Georgia State University Atlanta GA USA Department of Computer Science Saint Francis Xavier University Antigonish NS Canada Department of Computer Science HK University of Science and Technology Hong Kong China

Top-k query is a powerful technique in uncertain databases because of the existence of exponential possible worlds, and it is necessary to combine score and confidence of tuples to derive top k answers. Different semantics, the combination methods of score and confidence, lead to different results. U-kRanks and Global Top-k are two semantics of Top-k queries in uncertain database, which consider every alternative in x-tuple as single one and return the tuple which has the highest probability appearing at top k or a given rank. However, no matter which alternative (tuple) of an x-tuple appears in a possible world, it undoubtedly believes that this x-tuple appears in the same possible world accordingly. Thus, instead of ranking every individual tuple, we define two novel Top-k queries semantics in uncertain database, Uncertain x-kRanks queries (U-x-kRanks) and Global x-Top-k queries (G-x-Top-k), which return k entities according to the score and the confidence of alternatives in x-tuple, respectively. In order to reduce the search space, we present an efficient algorithm to process U-x-kRanks queries and G-x-Top-k queries. Comprehensive experiments on different data sets demonstrate the effectiveness of the proposed solutions.

关键词： Semantics Upper bound Heuristic algorithms Query processing Equations Artificial neural networks

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A Review-Based Reputation Evaluation Approach for Web Services

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Journal of Computer Science & Technology 2009年第5期24卷 893-900页

作者：李海华杜小勇 Member 田萱 Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China) MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China School of Information Science & Technology Beijing Forestry University Beijing 100083 China

Web services are commonly perceived as an environment of both offering opportunities and threats. In this environment, one way to minimize threats is to use reputation evaluation, which can be computed, for example, through transaction feedback. However, the current feedback-based approach is inaccurate and ineffective because of its inner limitations （e.g., feedback quality problem）. As the main source of feedback, the qualities of existing on-line reviews are often varied greatly from low to high, the main reasons include：（1） they have no standard expression formats, （2） dishonest comments may exist among these reviews due to malicious attacking. Up to present, the quality problem of review has not been well solved, which greatly degrades their importance on service reputation evaluation. Therefore, we firstly present a novel evaluation approach for review quality in terms of multiple metrics. Then, we make a further improvement in service reputation evaluation based on those filtered reviews. Experimental results show the effectiveness and efficiency of our proposed approach compared with the naive feedback-based approaches.

关键词： Web services reputation evaluation review quality measurement

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