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Density-based clustering for evolving uncertain data stream

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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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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WCSPMPD-stream: Mining weighted closed sequential patterns with pattern decay over data streams

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Journal of Computational Information Systems 2014年第1期10卷 435-442页

作者： Zeng, Qiang Han, Gaowei Li, Weina 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 of the previous incremental methods in data streams are deleting the old patterns and adding to the new patterns directly, which may delete useful patterns too early. Both different real data and the data occurring time can lead to the diverse importance, which are not considered at the same time in previous studies. Therefore, WCSPMPD-Stream, a weighted closed sequential pattern algorithm with pattern decay based on sliding windows over data streams is proposed in this paper. Firstly, the sliding windows are separated into several fragments to be mined respectively. Secondly, Time Weight Decay and No Updating Decay methods are designed to control the decay rate. Thirdly, a method of calculating the time weight is defined which varies linearly with the length of time interval between each stream fragment and the current stream fragment. Fourthly, an approach of getting the item weight is defined to calculate any sequence weight by the items in it. Fifthly, a new data structure WFPS-Tree is advanced to store and update the mining results. The experiments show that WCSPMPD-Stream algorithm overcomes the memory limitation and mines patterns with higher interest. And its execution time is reduced compared with the previous algorithms under the same conditions. Copyright © 2014 Binary Information Press.

关键词： Trees (mathematics)

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Inuence maximization algorithm based on genetic algorithm

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Journal of Computational Information Systems 2014年第21期10卷 9255-9262页

作者： Guo, Jingfeng Liu, Yuanying Shen, Jiali Wei, Zhenzhen Lv, Jiaguo College of Information Science and Engineering Yanshan University Qinhuangda China College of Information Technology Hebei University of Economics and Business Shijiazhuang China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

The inuence maximization problem is defined as providing a given initial integer k, mining top-k inuential nodes from a social network such that the spread of inuence in the network is maximized. Some existing studies are based on Greedy algorithm, but their time complexity is very high. In this paper, a different method based on Genetic Algorithms, denoted as MAGA is proposed. In the MAGA algorithm, the set of k nodes is seen as a candidate solution, and the expected inuence value as fitness. Use the genetic algorithm to get the optimal solution. Experiments show that the algorithm achieved a balance in inuence spread and running time.

关键词： Genetic algorithms

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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 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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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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Inference rules based on XML functional dependency and XML multi-valued dependency

Inference rules based on XML functional dependency and XML m...

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2014 International Conference on Future Information Engineering and Manufacturing Science(FIEMS 2014)

作者： Zhongping Zhang Shanshan Shi The School of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

ISBN: (纸本)9781138026445

There not only exists the relation of the functional dependency and multi-valued dependency in XML data constraint,but also exists the relation of data dependency obtained only from a certain existing relationship instance that is *** reducing data redundancy,in this paper,the concepts of XML functional dependency and XML multi-valued dependency based on the path expression in the Document Type Definition are *** then the inference rules on the condition of the coexistence of functional dependency and multi-valued dependency in XML are ***,the validity and completeness of the inference rules are *** above-mentioned fundamentally solves the problems of implication of functional dependency and multi-valued dependency in XML data,laying a theoretical foundation for XML database design.

关键词： Extensible Markup Language inference rules functional dependency multi-valued dependency

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Automatic simulated computation of remote sensing information model based on semantic

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Journal of Chemical and Pharmaceutical Research 2014年第7期6卷 521-529页

作者： Sun, Shengtao Wu, Aizhi The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Yanshan University Qinhuangdao China College of Vehicles and Energy Yanshan University Qinhuangdao China

This paper discusses the simulated computation methods of remote sensing information model, and tries to put forward a more available solution. It presents our research works on the description and simulation methods of remote sensing information model: the service on demand method provides a basic methodology of model analysis, the semantic relationship description of information processing procedure supports the basic knowledge of resource selection and matching based on user’s demand, and the hierarchical mapping method of remote sensing information model shows the transformation from abstract model to executable data processing workflow. © 2014, Journal of Chemical and Pharmaceutical Research. All rights reserved.

关键词： Semantics

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An efficient Subspace Clustering Algorithm based on Attribute Relativity and DBSCAN

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ICIC Express Letters 2014年第6期8卷 1677-1682页

作者： Zeng, Qiang Bai, Yunyue He, Haitao 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 Yanshan University No. 438 Hebei Ave. Qinhuangdao 066004 China

In the high-dimensional data traditional clustering algorithms tend to break down because of the curse of dimensionality, high cost of time, etc. This paper proposes a novel algorithm AReSUBCLU, an Effective Subspace Clustering Algorithm based on Attribute Relativity and DBSCAN. Active attribute is defined to reduce the dimension of data, and a subspace search tree which is constructed by attribute correlation matrix is defined to generate subspaces. Firstly, this method uses DBSCAN with the technique of weighted threshold to cluster on every active attribution. Secondly, the correlation matrix is constructed by computing the covariance of attributes. And the subspace search tree is constructed by the matrix which is used to measure the relativity of attributions. The set of nodes in each root branch of the tree is the interesting subspaces that the algorithm discovers. Finally, the subspace clusters are obtained through merging every cluster of active dimension with the usage of similarity of cluster. Evaluation with synthetic and real-world datasets demonstrates that AReSUBCLU has a good clustering quality, and capability of discovering subspace clusters of arbitrary shapes. © 2014 ISSN 1881-803X.

关键词： Clustering algorithms

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