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

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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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

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

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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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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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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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

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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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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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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Frequent jump pattern mining based on complex network

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Journal of Computational Information systems 2015年第17期11卷 6451-6458页

作者： Wang, Lei Jiang, Liya Dong, Jun Huang, Guoyan Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Hebei Normal University of Science and Technology Qinhuangdao China

Many researchers are devoted to find frequent pattern in static network. Due to these frequent patterns are usually defined as frequent existence patterns which satisfy given support threshold in network. However, the temporal feature in dynamic network is ignored. In this paper, frequent jump pattern (FJP) is proposed, it not only represents frequent pattern but also can show regular behavior in evolving social network. Furthermore, jump patterns are robust against noise in the analysis of dynamical process. We construct matrix of directed graph (MDG) to present the evolution of complex directed network over time. Moreover, a frequent jump pattern searching algorithm (FJPSA) based on MDG is proposed to find dynamic evolution rule of social network. FJP mining is a technique which can be used in many application fields, such as explaining biological evolution and mining customer behavior regularity in social network. Experimental results demonstrate that our method could effectively mining FJP-graph. © 2015, by Binary Information Press

关键词： Directed graphs

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Subspace clustering over high-dimensional data stream based on grid density and attribute relativity

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Advances in Information Sciences and Service Sciences 2012年第17期4卷 91-99页

作者： Huang, Guoyan Miao, Liyun Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao City 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China

The traditional clustering algorithms often fail to detect meaningful clusters in high-dimensional data space. To improve the above shortcoming, we propose GDRH-Stream, a clustering method based on the attribute relativity and grid density for high-dimensional data stream, which consists of an online component and an offline component. First, the algorithm filters out redundant attributes by computing the relative entropy. Then we define a weighted attribute relativity measure and estimate the relativity of the non-redundant attributes, and form the attribute triple. At last, the best interesting subspaces are searched by the attribute triple. On the online component, GDRH-Stream maps each data object into a grid and updates the characteristic vector of the grid. On the offline component, when a clustering request arrives, the best interesting subspaces will be generated by attribute relativity. Then the original grid structure is projected to the subspace and a new grid structure is formed. The clustering will be performed on the new grid structure by adopting an approach based on the density grid. Experimental results show that GDRH-Stream algorithm has better quality and scalability.

关键词： Relativity

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An algorithm based on temporary table for mining top-k closed 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20期6卷 582-589页

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

In this paper, we present an algorithm TKBT(top-k closed frequent mining based on TKTT) to mine top-k closed frequent itemsets in data streams efficiently. First according to the consecutive and changeable characteristics of the data from data streams in sliding window, a novel structure, BWT(bit-vector window table) is defined. In BWT horizontal direction we use bit vectors to express the transactions, record the count of items in the oldest, the newest window and all the windows a t current time, which decreases the calculating time of the items count when a new window slides in. In BWT vertical direction we set window partition, which makes us just need replace the oldest window information with the corresponding newest window when a new window comes. The construction of TKTT (top-k temporary table) is based on BWT. The itemsets in TKTT are ranked in a descending count order. TKBT can get top-k closed frequent itemsets by connecting the candidates in TKTT using top-down strategy. The candidate number is reduced by using closed itemset displace its subset and less connection times are contributed to the less runtime. Experiment results show that TKBT is very effective and scalable.

关键词： Data Streams Sliding Window Top-K Closed Frequent Itemsets

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