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Research on spectral clustering algorithms and prospects

Research on spectral clustering algorithms and prospects

引用

2010 2nd International Conference on Computer Engineering and technology, ICCET 2010

作者： Ding, Shifei Zhang, Liwen Zhang, Yu School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424463503

Along with the expansion and in-depth of the application domain of cluster analysis, one kind of new cluster algorithm called Spectral Clustering algorithm has been aroused great concern by scholars, Spectral Clustering algorithm is newly developing technique in the field of machine learning in recent years. Unlike the traditional clustering algorithms, this can solve the clustering of non-convex sphere of sample spaces and has globally optimal solution. This paper introduces the principle, the induction summary to the current research situation of Spectral Clustering algorithm as well as in various application domains. Firstly, the analysis and induction of some Spectral Clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, some typical Spectral Clustering algorithms have been selected to analyze and compare. Finally, it points out the key problems and future directions. © 2010 IEEE.

关键词： Cluster analysis

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Assignment reduct and its calculation method in inconsistent incomplete decision system

Assignment reduct and its calculation method in inconsistent...

引用

IEEE International Conference on Granular computing (GRC)

作者： Zuqiang, Meng Zhongzhi, Shi Hua, Qin College of Computer Electronics and Information Guangxi University Nanning 530004 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780769541617

Attribute reduction is a basic issue in rough set theory. But related researches on reduction in inconsistent incomplete decision system (IIDS) are currently rarely reported. In this paper, the concept of assignment reduct is extended from inconsistent complete decision system (ICDS) to IIDS and then the property of assignment-core attribute is analysed, and finally a method to calculate assignment reduct is put forward by using the heritable trait of assignment reduct. Example analysis shows that the proposed method is effective. This work provides an effective solution for attribute reduction in IIDS. © 2010 IEEE.

关键词： Rough set theory

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Nonparametric curve extraction based on ant colony system

Nonparametric curve extraction based on ant colony system

引用

作者： Tan, Qing He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Graduate University Chinese Academy of Sciences China

ISBN: (纸本)9781577354642

Curve extraction is an important and basic technique in image processing and computer vision. Due to the complexity of the images and the limitation of segmentation algorithms, there are always a large number of noisy pixels in the segmented binary images. In this paper, we present an approach based on ant colony system (ACS) to detect nonparametric curves from a binary image containing discontinuous curves and noisy points. Compared with the well-known Hough transform (HT) method, the ACS-based curve extraction approach can deal with both regular and irregular curves without knowing their shapes in advance. The proposed approach has many characteristics such as faster convergence, implicit parallelism and strong ability to deal with highly-noised images. Moreover, our approach can extract multiple curves from an image, which is impossible for the previous genetic algorithm based approach. Experimental results show that the proposed ACS-based approach is effective and efficient. Copyright © 2010, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Binary images

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引用

2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010

作者： Han, Xu Shi, Zhongzhi Niu, Wenjia Chen, Kunrong Yang, Xinghua Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China Graduate University of Chinese Academy of Sciences 100049 Beijing China

ISBN: (纸本)9780769541914

With the rapid development of XML language which has good flexibility and interoperability, more and more log files of software running information are represented in XML format, especially for Web services. Fault diagnosis by analyzing semi-structured and XML like log files is becoming an important issue in this area. For most related learning methods, there is a basic assumption that training data should be in identical structure, which does not hold in many situations in practice. In order to learn from training data in different structures, we propose a similarity-based Bayesian learning approach for fault diagnosis in this paper. Our method is to first estimate similarity degrees of structural elements from different log files. Then the basic structure of combined Bayesian network (CBN) is constructed, and the similarity-based learning algorithm is used to compute probabilities in CBN. Finally, test log data can be classified into possible fault categories based on the generated CBN. Experimental results show our approach outperforms other learning approaches on those training datasets which have different structures. © 2010 IEEE.

关键词： Bayesian networks

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Web service composition on the environment level

Web service composition on the environment level

引用

6th International Conference on Semantics, Knowledge and Grid, SKG 2010

作者： Cai, Guangjun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9780769541891

Service-oriented computing (SOC) promises a world of cooperating services loosely connected, creating dynamic business processes and agile applications. Service composition plays a very important role in it. One important problem related to service composition is about what the content of service description is. Most approaches focus on the description about web services themselves, and fail to consider the behaviour of the services in composition. This paper introduces a composition approach on the environment level. The basic idea of it is to develop a model-based approach by using the stability character of environment to encapsulate the changing of the service and the request. Our approach both facilitate to improve the composition efficiency and is more appropriate for compositing complex services or behaviour services based on its' behaviour. A running travel example is used to illustrate our approach. © 2010 IEEE.

关键词： Web services

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Sorted label classifier chains for learning images with multi-label

Sorted label classifier chains for learning images with mult...

引用

18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10

作者： Liu, Xi Shi, Zhiping Li, Zhixin Wang, Xishun Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781605589336

In the real world, images always have several visual objects instead of only one, which makes it difficult for conventional object recognition methods to deal with them. In this paper, we present a topologically sorted classifier chain method for learning images with multi-label. We first provide a means of generating a topo-logically sorted label chain ordering by employing a topological sort algorithm and then apply the chain ordering to the classifier chain model proposed by [1] to classify multi-label images. Our method can capture the correlations between labels very effectively due to the sorted label chain ordering and the advantages brought by classifier chain method. We evaluate the proposed method on Corel dataset and demonstrate the micro and macro F1 measures superior to the state-of-the-art methods. © 2010 ACM.

关键词： Object recognition

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Research of neural network algorithm based on FA and RBF

Research of neural network algorithm based on FA and RBF

引用

2010 2nd International Conference on Computer Engineering and technology, ICCET 2010

作者： Ding, Shifei Jia, Weikuan Su, Chunyang Chen, Jinrong School of Computer Science and Technology China University of Mining and Technology Xuzhou 221008 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100080 China

ISBN: (纸本)9781424463503

This paper proposes a radial basis function (RBF) neural network algorithm based on factor analysis (FA-RBF) with the architecture feature of RBF network when the data are high-dimensional and complex. By reducing the feature dimension of the original data, FA-RBF algorithm regards the data after dimension reduction as the inputs of the RBF network, and then trains and simulates the network. The algorithm obviously simplifies the network architecture. By analyzing an example, the results show when the algorithm's predicted precision is not reduced, the convergence velocity is improved, the running time is saved and the error of the predicted value is reduced. In order to test and verify the validity of the new algorithm, we compare it with the RBF neural network algorithm based on principal component analysis (PCA-RBF), the predicted results of FA-RBF algorithm are better than the results of RBF and PCA-RBF algorithm. © 2010 IEEE.

关键词： Radial basis function networks

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An efficient data mining framework on Hadoop using Java persistence API

An efficient data mining framework on Hadoop using Java pers...

引用

10th IEEE International Conference on Computer and information technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, 10th IEEE Int. Conf. Scalable computing and Communications, ScalCom-2010

作者： Lai, Yang Zhi, Shi Zhong Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100039 China

ISBN: (纸本)9780769541082

Data indexing is common in data mining when working with high-dimensional, large-scale data sets. Hadoop, a cloud computing project using the MapReduce framework in Java, has become of significant interest in distributed data mining. A feasible distributed data indexing algorithm is proposed for Hadoop data mining, based on ZSCORE binning and inverted indexing and on the Hadoop SequenceFile format. A data mining framework on Hadoop using the Java Persistence API (JPA) and MySQL Cluster is proposed. The framework is elaborated in the implementation of a decision tree algorithm on Hadoop. We compare the data indexing algorithm with Hadoop MapFile indexing, which performs a binary search, in a modest cloud environment. The results show the algorithm is more efficient than naïve MapFile indexing. We compare the JDBC and JPA implementations of the data mining framework. The performance shows the framework is efficient for data mining on Hadoop. © 2010 IEEE.

关键词： Data mining

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Voting conditional random fields for multi-label image classification

Voting conditional random fields for multi-label image class...

引用

2010 3rd International Congress on Image and Signal processing, CISP 2010

作者： Wang, Xishun Liu, Xi Shi, Zhiping Shi, Zhongzhi Sui, Hongjian Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781424465149

In our real world, there usually exist several different objects in one image, which brings intractable challenges to the traditional pattern recognition methods to classify the images. In this paper, we introduce a Conditional Random Fields (CRFs) model to deal with the Multi-label Image Classification problem. Considering the correlations of the objects, a second-order CRFs is constructed to capture the semantic associations between labels. Different initial feature weights are set to introduce the voting techniques for a better performance. We evaluate our methods on MSRC dataset and demonstrate high precision, recall and F1 measure, showing that our method is competitive. ©2010 IEEE.

关键词： Image classification

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Using global statistics to rank retrieval systems without relevance judgments

引用

作者： Shi, Zhiwei Wang, Bin Li, Peng Shi, Zhongzhi Information Retrieval Group Center for Advanced Computing Research Institute of Computing Technology Beijing 100190 China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100190 China

ISBN: (纸本)3642163262

How to reduce the amount of relevance judgments is an important issue in retrieval evaluation. In this paper, we propose a novel method using global statistics to rank retrieval systems without relevance judgments. In our method, a series of global statistics of a system, which indicate the percentage of its documents found by k out of all the N systems (k = 1, 2, ..., N), are selected, then a linear combination of the series of global statistics is utilized to fit the mean average precision (MAP) of the retrieval system. Optimal coefficients are obtained by linear regression. No human relevance judgments are required in the entire process. Compared with existing evaluation methods without relevance judgments, our method has two advantages. Firstly, it outperforms all early attempts. Secondly, it is adjustable for different effectiveness measurements, e.g. MAP, precision at n, and so forth. © 2010 IFIP.

关键词： information retrieval

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