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An improved algorithm for detecting community structure based on node similarity

Journal of Computational Information systems 2014年第9期10卷 3805-3813页

作者： Guo, Jingfeng Wei, Aiying Lv, Jiaguo Liu, Zhen College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China College of Information Science and Engineering Zaozhuang University Zaozhuang 277100 China

Detecting community structure based on node similarity cost lower time complexity, but they ignore the indirect relationships of nodes. We proposed an improved algorithm for detecting community structure based on node similarity (AINS). The algorithm presents a neighbor subgraph model based on akincore node pair, considering the direct and indirect relations between nodes. It introduced node indirect similarity to solve the problem which only pay attention to direct relationships, ensuring that the results meet the common definition of community structure (the number of edges between community is greater than that within community). The experimental results show that the AINS algorithm can obtain better community classification result effectively. © 2014 Binary Information Press.

关键词： Information systems

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The effects of DHPG stimulation on spikes and theta frequency oscillations and spike-theta oscillation relations in rat MSDB slice

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Journal of Convergence Information technology 2012年第17期7卷 442-449页

作者： Wen, Dong Ouyang, Gaoxiang Peng, Ce Li, Xiaoli Lu, Chengbiao Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Yanshan University Qinhuangdao Hebei China Institue of Electrical Engineering Yanshan University Qinhuangdao Hebei China National Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing 100088 China

Neuronal oscillations in the theta frequency range and spikes have been reported in many cortical areas in the information processing, but the role of spikes play in cortical processing remains unclear. The aim of this study was to examine the role of DHPG in the generation of oscillatory field activity at theta frequency in the medical septal diagonal band of Broca (MSDB) slice prepared from rat brain, and to determine the phase relationship between spikes and theta oscillations. The DHPG induced large amplitude field population spiking activity, which is correlated linearly with the field theta oscillations. The relationship between timing of spikes and the phase of theta oscillation was determined by an analysis of circular statistics. Further analysis with Rayleigh test revealed that the relationship of phase-locking between spikes and theta rhythm is of statistical significance. These data demonstrate that DHPG-sensitive neuronal networks within medial septum are able to generate theta oscillations which are modulated by large amplitude population spikes and phase-locking of spikes to the theta rhythm suggest the role of memory enhancement in the presence of mGluR1 activation.

关键词： Rats

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A novel algorithm for outlier mining in the internet of things

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ICIC Express Letters 2013年第1期7卷 215-219页

作者： Chen, Wei Liu, Yongshan Zhang, Yang College of Information Science and Engineering Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province No. 438 Hebei Ave. Qinhuangdao 066004 China

Most of the traditional outlier mining algorithms are not suitable for high dimensional datasets. However, the dimensionality of the dataset in the field of the Internet of Things is high. Therefore, Hypergraph-based Outlier Mining (HOM) algorithm is proposed to solve the problem. Two different hypergraph models are constructed in the HOM algorithm. One is itemset hypergraph. One-itemset in transaction database is itemset hypergraph's vertices, and the transactions are itemset hypergraph's hyperedges. The other one is transaction hypergraph. The transactions are its vertices, and frequent itemsets are the hyperedges. The outliers are found by hypergraph partitioning. Experiments show that HOM algorithm can effectively find the outliers in high dimensional space. ©2013 ICIC International.

关键词： Statistics

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Performance analysis of the centralized channel allocation strategy in cognitive radio networks

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ICIC Express Letters 2013年第7期7卷 2189-2196页

作者： Dai, Yu Jin, Shunfu Meng, Chao College of Information Science and Engineering Yanshan University No. 438 Hebei Ave Qinhuangdao 066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 Hebei Ave Qinhuangdao 066004 China

Based on the centralized channel allocation strategy in the Cognitive Radio Networks, considering the opportunistic channel access for Secondary Users, a discretetime queueing model with a general transmission time and possible transmission interruption is established. By using the method of an embedded Markov chain, the system model is analyzed in steady state, and the performance metrics in terms of the average latency, the throughput for the Secondary User, the channel switching ratio and the channel utilization are given accordingly. Analysis results and simulation experiments are provided to investigate the influence of the traffic load for the Secondary User and the arrival rate for the Primary User packets on the system performance. © 2013 ICIC International.

关键词： Cognitive radio

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Incremental mining of sequential patterns from multiple item set data streams

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ICIC Express Letters 2014年第10期8卷 2687-2692页

作者： Hao, Xiaobing Chen, Yuping Ren, Jiadong College of Information Science and Engineering Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 Hebei Ave. Qinhuangdao 066004 China

Sequential pattern mining has a wide range of applications in data streams. The real data involves multiple data streams and each data stream is itemset-sequence. However, most algorithms mine a single item in a single data stream. So IMASP algorithm (incremental mining of across-stream sequential patterns from multiple item set data streams) is presented to mine sequential patterns from multiple data streams and it is an incremental algorithm with single-pass scanning. What is more, the algorithm uses a multi-streams bitmap temporary table to store the sequence information in the current sliding windows. The partial substitution and the real-time counting of the item in old and new windows are adopted to improve the execution efficiency of the algorithm. A multi-streams Lexicographic Sequence Tree is used to store items, extended sequences and the support. Experimental results show that IMASP algorithm reduces time and memory consumption and has the efficiency and accuracy for mining across-stream sequential pattern from itemset sequence streams. © 2014 ICIC International.

关键词： Efficiency

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A weighted clustering algorithm based on user’ access sequence similarity

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ICIC Express Letters 2016年第10期10卷 2363-2370页

作者： Li, Weina Han, Gaowei Ren, Jiadong College of Information Science and Engineering Yanshan University No. 438 Hebei Ave. Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 Hebei Ave. Qinhuangdao066004 China

Consider traditional clustering algorithms seldom had a research on users’ visit behavior and content, and they cannot cluster users with similar visit behavior into a community easily. Behavior of user cannot cluster users with similar visit behavior i also reflect usery. Behavior Therefore, setting about the log of user’s access behavior, in view of visitor volume, sequences of accessing keywords and time interval between keywords, a weighted clustering algorithm based on sequence similarity which is called K-Similar is put forward. Starting from behavior of user’s access to document, users are clustered, and important users are divided into a community. Then, literature needs of users in the same cluster are mined. This algorithm has been verified in users users are log data in CNKI, available for academic publishing. Experimental results show that K-Similar is more efficient in efficiency and accuracy. It can obtain more interesting user communities and show academic publishing demand of special users. © 2016 ICIC International.

关键词： Clustering algorithms

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An efficient algorithm for mining maximal frequent sequences by Top-Down Delay Decomposition method

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ICIC Express Letters 2014年第7期8卷 2007-2012页

作者： Wang, Qian Ren, Jiadong Liu, Qiang College of Information Science and Engineering Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 Hebei Ave. Qinhuangdao 066004 China

Maximal frequent sequence mining is an important research issue which has realized the highly compressed storage of frequent sequences. At present, most algorithms are based on bottom-up method and large numbers of candidate sequences are generated. In this paper, an algorithm based on Top-down Delay Decomposition for mining Maximal Frequent Sequence is proposed. TDD MFS uses top-down delay decomposition method, sequences from original database and decomposition are expressed by different tuples to record their direct and indirect occurrence counts. Longest infrequent sequences are recursively decomposed and the decomposition of infrequent sequences that have frequent super sequences is avoided. Only one database scan is needed, no candidate sequences are generated and sequence decomposition times are reduced. Experiment result shows its high efficiency and good scalability. © 2014 ISSN 1881-803X.

关键词： Delay decomposition Frequent sequence Maximal frequent sequence

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A batch-based algorithm for mining weighted sequential patterns in data streams

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ICIC Express Letters 2014年第7期8卷 1821-1826页

作者： Hao, Xiaobing Li, Yabin Huang, Guoyan Ren, Jiadong College of Information Science and Engineering Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 Hebei Ave. Qinhuangdao 066004 China

In this paper, we proposed a new sequential pattern mining algorithm called WSPD for mining weighted sequential patterns in data streams. The algorithm produces no false negatives and places a bound on the error of the false positives. WSPD could also satisfy the user's dynamic request for mining weighted sequential patterns from any batches. Experimental results show that WSPD algorithm is effective and scale linearly for mining weighted sequential patterns in data streams. © 2014 ISSN 1881-803X.

关键词： Trees (mathematics)

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Efficiently mining top-k high utility path patterns in software dynamic call graph

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ICIC Express Letters 2016年第1期10卷 211-218页

作者： He, Haitao Yin, Tengteng Wu, Hongfei Ren, Jiadong College of Information Science and Engineering Yanshan University No. 438 West Hebei Ave Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 West Hebei Ave Qinhuangdao China

Software behavior mining is a very meaningful work. Finding that desirable patterns can assist the program maintainers to comprehend the software adequately. Although the existing high utility pattern mining algorithms can discover all the patterns satisfying a given minimum utility, it is often dicult for users to set a proper minimum utility. In this paper, an ecient algorithm TUPP is presented to mine top-k high utility path patterns in software dynamic call graph. In TUPP, software paths are extracted from software dynamic call graph and the utility of functions is dened rstly. Secondly, a novel structure called PUAList is put forward. The structure stores both the adjacency items and the utility information which is heuristic information for pruning the search space. In addition, two strategies for raising the threshold and one pruning strategy are incorporated into TUPP to increase the eciency. At last, our experiments are conducted on both synthetic and real datasets. The results show that TUPP incorporating the eciency-enhanced strategies demonstrates impressive performance. © 2016 ICIC International.

关键词： Graph theory

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An energy saving strategy in digital cognitive radio networks and its performance assessment

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ICIC Express Letters 2016年第6期10卷 1367-1373页

作者： Jin, Shunfu Chen, Si School of Information Science and Engineering Yanshan University No. 438 Hebei Ave Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 Hebei Ave Qinhuangdao066004 China

With the explosive growth of wireless application, how to improve the spectrum efficiency as well as reduce the communication consumption is a hot topic of research. In this paper, we propose a novel energy saving strategy in digital cognitive radio networks (CRNs). The key idea of this strategy is to set the base station (BS) of CRNs into sleep state when there are not any packets to be transmitted. Accordingly, we develop a Markov chain model to assess the average latency of secondary user (SU) packets and the energy saving ratio of system. Finally, we provide numerical results to show that there is a tradeoff when setting the sleep timer length for the proposed energy saving strategy. © 2016 ISSN.

关键词： Energy conservation

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