检索结果-内蒙古大学图书馆

K-means sequence clustering algorithm based on Two-stage center sequences selection

Journal of Computational Information systems 2014年第6期10卷 2351-2358页

作者： Wu, Di Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Department of Information and Electronic Engineering Hebei University of Engineering Handan 056038 China Key Lab of Computer Virtual Manufacturing and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China

K-means sequence clustering is a hot topic in data mining. However, the clustering quality of K-means is affected heavily by outliers and noises. In this paper, a new sequence similarity measurement, sequence density and Q neighborhoods of sequence are deffned and then a novel K-means sequence clustering algorithm TCSSK-means (Two-stage center sequences selection-based K-means) is proposed. In the first stage, the sequence with the minimum density neighborhood is viewed as initial clustering center, and the distances between each initial clustering center are as far as possible, the KICCSS (K initial clustering center sequences set) is obtained. In the second stage, by selecting the mean sequence of the larger similarity sequences set, which meets certain conditions with clustering centers in the T-1-th round, K clustering centers of sequence databases in the T-th round are updated. Repeat the renewal process of sequence clustering centers until the sequence clustering criterion function converges. K final sequence clusters are gained finally. The experimental results and analysis show that the performance of TCSSK-means is better in clustering quality and running time. Copyright © 2014 Binary Information Press.

关键词： Data mining

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The constructions of ternary sequence pairs with two-level autocorrelation

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Journal of Information and Computational Science 2014年第13期11卷 4605-4612页

作者： Jia, Yanguo Duan, Xiaobei Shi, Yan Shen, Xiumin Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao China

In this letter, a periodic autocorrelation signal is presented, which is the ternary sequence pair with two-level autocorrelation. The methods of constructing ternary sequence pairs based on binary sequence pairs and cyclotomy are presented by us. Then we have a discussion about perfect binary sequence pairs, pseudorandom binary sequence pairs, difference set pairs and ternary sequence pairs. These signals enrich the theory of sequence pair discrete correlation signals. Copyright © 2014 Binary Information Press.

关键词： Autocorrelation

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Robust recommendation algorithm based on the identification of suspicious users and matrix factorization

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Journal of Information and Computational Science 2014年第13期11卷 4769-4777页

作者： Yi, Huawei Zhang, Fuzhi Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao China

The existing recommendation algorithms have lower robustness against shilling attacks. With this in mind, in this paper we propose a robust recommendation algorithm based on the identification of suspicious users and matrix factorization. We first give the computational methods of Deviation Degree about the Number of Ratings (DDNR) and Average Similarity of Neighbors (ASN) according to the distribution of users' ratings. Then we identify suspicious users based on the differences of computational results of users' DDNR and ASN. Finally, we introduce the matrix factorization technology which incorporates indicator function and the identification results of suspicious users to make recommendations for users. Experimental results show that the proposed algorithm not only improves the recommendation accuracy, but also has better robustness. Copyright © 2014 Binary Information Press.

关键词： Matrix algebra

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New construction of quaternary sequence pairs with even period

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Journal of Information and Computational Science 2014年第13期11卷 4613-4621页

作者： Jia, Yanguo Shi, Yan Duan, Xiaobei Shen, Xiumin Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao China

In the paper, we present a new method for constructing a class of quaternary sequence pairs with even period 2N from the known binary sequence pairs with odd period N by using the reverse Gray mapping and interleaving technique. The distribution of correlation function of the proposed quaternary sequence pairs is also derived, which is decided by the properties and the periods of the known binary sequence pairs. Copyright © 2014 Binary Information Press.

关键词： Mapping

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Software fault localization via mining program dynamic execution graph

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Journal of Computational Information systems 2014年第19期10卷 8563-8573页

作者： Zhang, Kai Yang, Ke Zhou, Lianbo Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

This paper is concerned with the problem of locating the code area related to software potential fault quickly and accurately in software testing period. A new method Sig BB based on graph model is proposed for mining the suspicious fault nodes from the passing and failing execution graphs. Representing each execution of a program as a graph, the graphs are divided into the passing and failing sets. By extracting the most representative passing and failing graphs based on these sets, the discriminative sub-graph is mined between the two representative graphs. First, Sig BB searches the max common graph, and then gets the opposite nodes set. The discriminative sub-graph is obtained by organizing and extending the set finally. Since the detected code scale is associated with the sorting of suspicious nodes, a suspicious metric strategy is also designed to sort the nodes in the discriminative sub-graph. Experimental results indicate that our method is both effective and efficient for software fault localization.

关键词： Software testing

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Real-time simulation of action-driven 3D subtle facial expressions

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Journal of Computational Information systems 2014年第5期10卷 2137-2144页

作者： Tang, Yong Liang, Haiyan Guo, Dan Lv, Mengya Zhao, Jing College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China

While the large-scale deformations such as the Laplacian deformation method could not synthesize new expressional details, this paper proposes a method for simulating different subtle facial expressions based on the Kinect acquisition system and the facial expressional reconstruction technique. Firstly, we generate the coarse skin textures with pore details and those normal maps. Secondly, we use blending wrinkle maps to render animated details on a human face based on a GPU shader program. Finnaly, according to the action units captured by the Kinect, we calculate the dynamic texture mapping and the weight of wrinkles, and apply the wrinkles function to simulate the motion of wrinkles. Experimental results show that our method can generate facial details in real time. The method suits the fields such as digital entertainment and video conferences. © 2014 Binary Information Press.

关键词： Blending

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IMSPMIS-Stream: incremental mining of top-k short sequential pattern over multiple item set streams

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Journal of Computational Information systems 2014年第6期10卷 2305-2312页

作者： Hao, Xiaobing Han, Gaowei Chen, Yuping Wang, Peilong Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China

Many previous algorithms in data streams are about single stream, which can only process single items. The algorithms about data streams are always extended by sequential pattern algorithms about static database, they can't satisfy the requirement of scanning streams only once and online mining. Moreover, top-k short sequential pattern hasn't been studied yet. Therefore, IMSPMIS-Stream algorithm is proposed to mine online multiple streams with the single scan by the incremental method in this paper. The streams are continuous transactions, which is also called item set streams. Several streams are mined interactively based on sliding windows at the same time. An approach to count the support of every sub item set is designed to get the support during one scan. The short sequential pattern is deffned, whose item sets are shorter than all the item sets with the same occurring time. When the arrival transactions reach to the capacity of the window, sequential patterns will be output by combining top-k short item sets. The experiments show that the IMSPMIS-Stream algorithm overcomes the memory limitation and its execution time is reduced compared with some previous algorithms under the same conditions. Copyright © 2014 Binary Information Press.

关键词： Information systems

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A Robust Collaborative Recommendation Algorithm Based on k-distance and Tukey M-estimator

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China Communications 2014年第9期11卷 112-123页

作者： YI Huawei ZHANG Fuzhi LAN Jie School of Information Science and Engineering Yanshan University Qinhuangdao 066004 Hebei Province E R.China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 P. R. China Liaoning University of Technology Jinzhou 121001 Liaoning Province P. R. China

The existing collaborative recommendation algorithms have lower robustness against shilling *** this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey ***,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor *** influence of attack profiles on the recommendation results is reduced through adjusting similarities among ***,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature ***,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization *** results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.

关键词： shilling attacks robust collaborative recommendation matrix factori-zation k-distance Tukey M-estimator

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Mining time-interval weighted closed sequential patterns based on memory indexing

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Journal of Computational Information systems 2014年第1期10卷 293-300页

作者： Zeng, Qiang Chen, Dengxi Ren, Jiadong Han, Gaowei College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China

General weighted sequential pattern mining algorithms ignore or do not make good use of the time and time-interval information of data elements. Besides some algorithms require to scan the database many times or build temporary databases. To solve these problems, we propose a memory-based algorithm MITWCSpan (Memory Indexing for time-interval Weighted Closed Sequential pattern mining) for timeinterval weighted closed sequential pattern mining. The algorithm takes full account of the importance of the time-interval of data elements. Moreover, an improved index set based on time-interval, p-tidx, is defined. During mining process, the algorithm adopts the find-then-index technique recursively to find the items which can constitute a time-interval weighted sequential pattern and construct p-tidx for the possible sequential pattern. Finally the algorithm uses closing detection to get the whole time-interva l weighted closed sequential patterns. The experimental results show that the algorithm is more efficient in finding more important sequential patterns. Copyright © 2014 Binary Information Press.

关键词： Indexing (of information)

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Density-based clustering for evolving uncertain data stream

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Journal of Computational Information systems 2014年第1期10卷 419-426页

作者： He, Haitao Zhao, Jintian The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China

The current clustering algorithms for evolving uncertain data stream are sensitive to user specified threshold, and unstable in noise processing. In this paper, DUStream is presented, a density-based algorithm for discovering clusters in evolving uncertain data stream. Probability distance is introduced as a new similarity measure, giving consideration to probability attribute and distance attribute. Probability Radius is used as a self-adaption dynamic threshold to reduce the effect of user specified input. The experimental results demonstrate the effectiveness and Effciency of the algorithm on Artificial and real data sets. Copyright © 2014 Binary Information Press.

关键词： Probability

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