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A density grid-based uncertain data stream clustering algorithm

Journal of Computational Information systems 2014年第9期10卷 3619-3626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 existing grid-based uncertain data stream clustering algorithms are fast but low-accuracy, and sensitive to user-specified threshold. In order to solve the above problems, a density grid-based uncertain data stream clustering algorithm UG-Stream is proposed in this paper. In UG-Stream algorithm, a dynamic threshold is defined by taking uncertainty and grid feature into account, dense grid can be distinguished by the threshold. The probability variance is defined to describe the distribution of internal data points in grid. If grid distribution can be taken as uniform, dense grid can be classified as core dense grid by probability variance. Core dense grid can be clustered directly, the rest of dense grids will be merged into current existing clusters by probability center distance. Contrast experiments show that UG-Stream algorithm is superior to UMicro algorithm in both clustering accuracy and clustering rate. © 2014 Binary Information Press.

关键词： Clustering algorithms

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virtual grid-based clustering of uncertain data on vulnerability database

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Journal of Convergence Information technology 2012年第20期7卷 429-438页

作者： Dong, Jun Cao, Mengmeng Huang, Guoyan 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

Most existing vulnerability taxonomy classifies vulnerabilities by their idiosyncrasies, weaknesses, flaws and faults et al. The disadvantage of the taxonomy is that the classification standard is not unified and there is overlap classification phenomenon in vulnerability taxonomy. In order to solve the problem, we will propose an algorithm VUNClique, virtual Grid-based Clustering of Uncertain Data on vulnerability database. Firstly, this paper transforms the vulnerability database into uncertain dataset using the existing vulnerability database pretreatment model. Secondly, we define a virtual grid structure, the cells are divided into real cells and virtual cells, but only the real cells which contain data objects stored in memory. The probability attribute value similarity is defined to deal with the similarity of non-numeric attributes, which compares the number of non-numeric attributes with the same value between tuples to measure the similarity. We provide a secondary partition algorithm to improve the similarity between the tuples in the same cell, the algorithm merges a tuple into it's high-density neighbor cell which has the maximum value of probability attribute value similarity with it. Then, a novel identify cluster algorithm is provided to cluster the high-density real cells. It can identify clusters of arbitrary shapes by traversing real cells twice. Finally, performance experiments over the uncertain dataset transformed by NVD vulnerability database. The experiments results show that VUNClique can find clusters of arbitrary shapes, and greatly improve the efficiency of clustering.

关键词： Cells

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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

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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A high dimensional data clustering algorithm based on hypergraph

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Journal of Computational Information systems 2012年第23期8卷 9839-9846页

作者： Liu, Yongshan Chen, Wei Shang, Xuehui Lv, Yongteng Han, Yuanyuan Liu, Chang 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

High dimensional data clustering is an important issue for data mining. Firstly, the records in the dataset are mapped to the vertices of hypergraph, the hyperedges of hypergraph are composed of the vertices which have the same value on one certain attributes. Then a multilevel hypergraph partitioning algorithm is used to find k parts of the hypergraph. Finally, the clusters with good quality are choosed out by computing the value of clusters validity. The experimental results show that the new algorithm can efficiently construct high quality clusters. ©2012 by Binary Information Press.

关键词： Data mining

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Robust recommendation algorithm based on user rating matrix block and modified LTS-estimator

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Journal of Information and Computational Science 2014年第6期11卷 1889-1898页

作者： Xu, Yuchen Liu, Zhen Zhang, Fuzhi School 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

The most widely-used collaborative recommendation algorithms are vulnerable to shilling attacks. To this end, in this paper we propose a robust recommendation algorithm based on user rating matrix block and modified LTS-estimator. Firstly, we construct user rating matrix blocks using user rating matrix block algorithm based on k-median clustering. Secondly, we apply the modified LTS-estimator to matrix factorization model in order to produce user feature matrix and item feature matrix. Finally, we devise a robust recommendation algorithm to generate recommendations for the target users. Experimental results on the MovieLens dataset show that the proposed algorithm outperforms the existing methods in terms of both the prediction accuracy and robustness. © 2014 Binary Information Press.

关键词： Matrix factorization

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An energy-efficient routing algorithm of zigbee network

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Journal of Computational Information systems 2012年第23期8卷 9873-9879页

As regard to the case of extending the lifetime of zigbee network, the defination of node's boundary is proposed. First, all the information for node's boundary is stored when zigbee network is built. Then, the packet between the nodes which are in the node's boundary is transferred directly. The packet between the nodes whose boundaries are adjacent is transferred through the node with maximum energy. The exprimental results show that the new algorithm can efficiently extend the lifetime of zigbee network. ©2012 by Binary Information Press.

关键词： Zigbee

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Sequential pattern mining with gap constraints for discovery of the software bug features

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Journal of Computational Information systems 2014年第2期10卷 673-680页

作者： Zeng, Qiang Chen, Yuping Han, Gaowei 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

In order to process the software bug feature sequences, this paper presents a gap-constrained sequential pattern mining algorithm, MEMIGCSP algorithm. The length of the interval between items is limited in the original sequences so as to find interested sequences. The gap constrained prefix index set (P-Gidx) based on memory indexing provides the position information of the bug sequence patterns. Strategy of divide-and-conquer is used to deal with the sequences which are in the P-Gidx and others respectively. What's more, pattern growth is advanced to extend the prefix pattern after the local extension items are searched from the forward and backward space, thus the complete set of sequential patterns can be found. The performance evaluation demonstrates the scalability of MEMIGCSP and shows the efficiency and accuracy for mining sequential pattern with gap constraints. Copyright © 2014 Binary Information Press.

关键词： Indexing (of information)

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Multi-Update Patterns and Validity Verification for Robust Visual Tracking

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Journal of computers (Taiwan) 2020年第4期31卷 77-90页

作者： Wen, Jia Wang, Hong-Jun Li, Yan-Nan Zhao, Ji-Wei Yang, Yu-Ying School of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province China Key Laboratory of Software Engineering of Hebei Province China

The Object tracking is a challenging problem in computer vision field. Now, deep learning has made outstanding achievements in feature extraction. There are already some examples of deep learning applications in visual tracking. But, they almost paid attention to precision and sacrificed efficiency. So we propose a tracker named adaptive combination tracker(ACT), based on neural network model, which gives consideration to both precision and efficiency. Our ACT tracker gets rich features offline through Stacked Denoising Autoencoders (SDAE) and transfers them to online tracking. In tracking, a dynamic particle filter algorithm is proposed to speed up tracking. We update our tracker with different patterns according to the change in appearance. In order to ensure efficiency, our tracker regulates speed adaptively. Our tracker regulates speed precisely to avoid drift caused by fast motion as much as possible, and confirm and optimize the effectiveness of the results by comparison with previous target. We evaluate our tracker on OTB2013 datasets. Extensive experiments on OTB2013 datasets demonstrate that the proposed tracker performs favorably in relation to the state-of-the-art methods. © 2020 computer Society of the Republic of China. All rights reserved.

关键词： Efficiency

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New methods of constructing quaternary sequence Pairs

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ICIC Express Letters 2016年第9期10卷 2177-2183页

作者： Jia, Yanguo Shen, Xiumin Duan, Xiaobei Song, Xiaofei School of Information Sciences and Engineering Yanshan University China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 Hebei Ave. Qinhuangdao066004 China

In this letter, new methods of constructing quaternary sequence pairs are presented based on binary sequence pairs with two-level autocorrelation, almost perfect binary sequence pairs and cyclic shift sequences. The quaternary sequence pairs con- structed by these methods are periodic autocorrelation signals. These methods are handy and exible, and can greatly enrich the theory of quaternary sequence pairs. © 2016 ICIC International.

关键词： Autocorrelation

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Time sequential influence maximization algorithm based on neighbor node influence

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High technology Letters 2022年第2期28卷 153-163页

作者： CHEN Jing QI Ziyi LIU Mingxin School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004P.R.China Hebei Key Laboratory of Virtual Technology and System Integration Qinhuangdao 066004P R.China Hebei Key Laboratory of Software Engineering Qinhuangdao 066004P R.China College of Electronic and Information Engineering Guangdong Ocean UniversityZhanjiang 524088P.R.China

In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this *** is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information ***,the propagation probability between nodes is calculated by the improved degree estimation ***,the weighted cascade model(WCM) based on static social network is not suitable for temporal social ***,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node *** combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it ***,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.

关键词： neighbor node influence time sequential social network influence maximization(IM) information propagation model

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