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A RIHS-tree-based algorithm of mining frequent closed itemsets in high dimensional datasets

Journal of Convergence Information technology 2012年第21期7卷 467-476页

作者： Huang, Guoyan Chen, Shuai Ren, Shenyuan Dong, Jun College of Information Science and Engineering Yanshan University Qinhuangdao Hebei 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China College of Computer Science Sichuan University Chengdu Sichuan 610000 China

In order to fully utilize the minimum support threshold to prune the rowset search space and avoid scanning the whole dataset in close-checking, we proposed two new data structure, Rowset-Itemset-Hybrid-Search-Tree (RIHS-Tree) and Frequent-Closed-Itemset-Rowset-Tree (FCIR-Tree). The former stores the rowsets which share common rowid prefixes, according to some predefined order of rowids. Each path from the root node to the leaf node represents a rowset in which the rowids increase. The later also stores the large rowsets which also share common rowid prefixes. The corresponding closed itemsets are stored in the nodes carrying the last rowids in rowsets. Moreover, a new algorithm, RIHS-Tree-based algorithm for mining frequent closed patterns (FCIM-RIHS), is proposed. We translate the whole dataset into a transposed table, construct RIHS-Tree according to the rowsets in the transposed table and use a bottom-up search strategy to traverse RIHS-Tree. In the process of mining, we use the rowsets-inclusion strategy to implement pattern growth and then obtain large rowsets with their corresponding frequent closed itemsets. In FCIR-Tree, if the nodes in the subtree rooted by the nodes carrying the last rowid in the rowsets do not contain the found itemsets, the found itemsets are frequent closed itemsets. The experiment results with dataset demonstrate that FCIM-RIHS has highly efficiency.

关键词： Trees (mathematics)

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An identity authentication algorithm in the object name service of the internet of things for heat meter

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Advances in Information Sciences and Service Sciences 2012年第22期4卷 174-181页

作者： Han, Yuanyuan Liu, Yongshan Chen, Wei Liu, Chang Shang, Xuehui Lv, Yongteng The Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Yanshan University Qinhuangdao City 066004 China College of Information Science and Engineering Yanshan University Qinhuangdao City 066004 China

In the environment of the Internet of Things for heat meter, insecurity of the object name service (ONS) is likely to lead to the leakage of user privacy. In order to solve this problem, an identity authentication algorithm is proposed. First, the certificate authority may generate parameters of elliptic curve and its own public key and private key, and announce the parameters and its own public key, so that the registered users can inquire and use them. Second, the client registers at the certificate authority. The certificate authority will generate the public key of the client and the evidence of the client's private key which is signed by private key of the certificate authority. If the generated evidence of private key passes verification of the client, the client will generate its own private key. When a client sends a query request to an ONS server, the ONS server will authenticate identity of the client. If the client gets through the authentication, the ONS server will respond to the query request. The experimental result shows that this algorithm has high security and can avoid the ONS server providing query service for illegitimate client, so as to ensure the security of user privacy.

关键词： Internet of things

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A new estimator analysis method to synchronization among multivariate neural series of mild cognitive impairment

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2012年第23期6卷 868-874页

作者： Wen, Dong Guan, Xinyong Chen, Ruihong The Key Laboratory for Computer Virtual Technology System Integration of HeBei Province Yanshan University Qinhuangdao Hebei 066004 China Liren College Yanshan University Qinhuangdao Hebei 066004 China Division of General Internal Medicine Xiantao First People's Hospital Xiantao Hubei 433000 China

Synchronization decay of resting state EEG has shown that cognitive dysfunction in mild cognitive impairment (MCI) was relevant to a loss of functional connectivity in intermediate frequency bands. A new estimator analysis method called new S estimator (NSE) proposed recently by us quantifies synchronization between neuronal signals at multiple *** paper meant to explore the behavior of synchronization of multichannel EEG in MCI and healthy normal controls at rest, and preliminarily make clear the clinical significance of the NSE in MCI *** (EEG) were recorded from 10 MCI patients and 12 age-matched healthy normal controls (NC). NSE values were computed both across the all frequency band and separately in the delta, theta, alpha, beta (including beta1, beta2 and beta3), and gamma bands. The Montreal Cognitive Assessment (MoCA) was used to assess the symptom severity of MCI patients and *** values in the alpha and beta1 bands were significantly lower in MCI patients than in NC. NSE values in the alpha and beta1 bands were positively correlated with the MoCA scores in all participants (MCI and NC). In MCI patients, NSE values in the alpha and beta1 bands were also positively correlated with MoCA *** results suggest that NSE values are a useful correlate of EEG synchronization in MCI patients.

关键词： Synchronization

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An algorithm based on bit vector decomposition and hash linklist for mining frequent patterns in data streams

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2012年第22期6卷 570-579页

作者： Ren, Jiadong Zhang, Yuanyuan Yu, Shiying College of Information Science and Engineering Yanshan University Qinhuangdao Hebei 066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China Hebei Provincial S and T Management Information Center 159 Dongfeng Road Shijiazhuang 050021 China

Most of algorithms based on bit vector for mining frequent pattern always produce candidate itemsets and scan the same data repeatedly;this increases the running time and the space consumption. In this paper, an algorithm BVDAHL based on bit vector decomposition and hash linklist for mining frequent patterns on data streams which solves the above issues, is proposed. The itemsets (transactions) whose corresponding number of 1 is k, denoted as k-one itemsets (k-one transactions). The arrival transactions are converted into bit vectors, and permutations and combinations are used to decompose converted results, then the decomposed itemsets are stored in the hash linklist. In hash function, according to the position of 1 of bit vector to compute the address, if the transaction has same address and same content that already exists in data domain, then the value adds one in count domain, otherwise the pointer in pointer domain points to the next node directly, it is no need to deal with conflict. Afterwards the property of anti-monotonic will be used to prune the k+1-one transactions in bit vector table if there exists infrequent k-one itemsets that decomposed by such k+1-one transactions. Then the algorithm repeats the above processes until the transaction in bit vector table can not be decomposed. Finally we will receive the frequent itemsets by comparing the obtained value of count domain in hash linklist with minSup. Experiment results show that BVDAHL is very efficiency and scalable.

关键词： Hash functions

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A novel grouping aggregation algorithm for online analytical processing

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2012 National Conference on Information technology and computer Science, CITCS 2012

作者： Chen, Wei Liu, Yongshan Wang, Ning College of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Qinhuangdao China

ISBN: (纸本)9789491216381

As regard to improve the efficiency of grouping aggregation calculation, the data is compressed by using binary encoding, and the dimension hierarchical grouping attribute set encodings of each dimension table are calculated by using the dimension hierarchy tree. Then different encodings are put into temporary table to be sorted and grouped, and the grouping sequence numbers of fact table records which satisfy the query conditions are computed. Finally, the buffer is located by the grouping sequence number and the grouping aggregation calculation is completed. Theoretical analysis and experimental results show that the proposed algorithm can significantly improve the efficiency of grouping aggregation calculation. © 2012. The authors - Published by Atlantis Press.

关键词： Efficiency

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Multi-resolution terrain rendering seamlessly by function curve fitting

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Journal of Computational Information Systems 2012年第24期8卷 10399-10409页

作者： Nie, Junlan Guo, Dongliang Kong, Lingfu Wang, Yanfen The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China

During the process of real-time multi-resolution terrain rendering, avoiding cracks between LOD (Level of Detail) has always been a tedious task that requires extra complicated handling. In this paper, we propose a novel algorithm called Function Curve Fitting (FCF) to repair T-junction cracks of regular data-based LODs. FCF, as a time-efficient algorithm, uses regular grid as rendering element, could be conveniently integrated with existing algorithms and achieve seamless meshes with excellent image quality and frame rates. Compared with other existing LOD crack-repairing algorithms based on terrain rendering, the experiments on surface quality and rendering performance demonstrate the effectiveness of our FCF approach. © 2011 by Binary Information Press.

关键词： Curve fitting

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An algorithm based on compressed tree and bit vector table for mining frequent patterns on uncertain data streams

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Journal of Convergence Information technology 2012年第20期7卷 389-398页

作者： Dong, Jun Zhang, Yuanyuan Song, Wei Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao Hebei 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China

Most of algorithms based on tree structure for mining frequent pattern on uncertain data streams always store a large number of tree nodes, and record the corresponding information of data streams which can cause massive information storages. In this paper, an algorithm CTBVT based on compressed tree and bit vector table for mining frequent patterns on uncertain data streams, is proposed. The uncertain data streams are initialized to probability-vector table, in the table, the items are represented by transactions, unlike other bit vector tables the occurrence probabilities of items are stored in it. When the window slides, all the columns in probability-vector table are left shift m bits at the same time and m is the number of transactions in the window. We also propose compressed tree in which the items with different probabilities are stored in the same tree nodes, which will reduce the number of tree nodes significantly, then the items and its probability in the tree node correspond to the bit vector table are converted into binary bit vector, the number of 1s in the binary bit vector is the frequency of the tree node. Afterwards, each leaf node of the tree is connected to an array which is used to store the combination of all items and their expected support in the path. The leaf nodes are stored in the LeafList. Finally, we scan the arrays that are linked to the leaf nodes in the LeafList and compare the expected support that is stored in the array with a minimum support threshold minSup to get all the frequent itemsets, mining time will reduce dramatically. Experiment results show that CTBVT is very efficiency and scalable.

关键词： Probability

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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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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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An algorithm based on bit complementary tree for mining closed frequent itemsets

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International Journal of Advancements in Computing technology 2012年第22期4卷 427-435页

作者： Ren, Jiadong Yang, Xuan Dong, Jun College of Information Science and Engineering Yanshan University Qinhuangdao City066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China

In recent years, closed frequent itemsets mining has become a hot topic. In this paper, we present an algorithm BCTCF, which is based on Bit complementary tree (BCTree) in order to mine closed frequent itemsets efficiently. First we adopt bit vectors to compress the database and define a novel structure, BCTree, in which a node stores two bit vectors that are complementary and each path is given a prime value. Based on the left-most bit in the bit vectors we adopt a divide-and-conquer strategy which handles the itemsets separately and then according to the prime unique feature we can get the closed frequent itemsets quickly and it makes us need not to mine all the frequent itemsets first. Both the divide-and-conquer strategy and prime unique can decrease the runtime. Experiment results show that BCTCF is very effective and scalable.

关键词： Forestry

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