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A clustering algorithm for heterogeneous objects based on information dissimilarity

Journal of Convergence Information technology 2012年第22期7卷 93-100页

作者： Chen, Ziyang Wang, Xuefeng 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

Due to the introduction of weight of attributes, most of existing dissimilarity measures can not accurately reflect the difference between two heterogeneous objects, and then clustering quality was decreased. In this paper, we present HIDK-means, an approach for clustering heterogeneous data based on information dissimilarity. At first, the algorithm defines heterogeneous information dissimilarity between two heterogeneous objects based on Kolmogorov information theory, and calculates approximately heterogeneous information dissimilarity by a universal probability of an object. Then, in the clustering process, the algorithm selects the initial cluster centers by maximum sum of dissimilarity. After that, each remaining object is assigned to a cluster center which has the smallest dissimilarity with it and the criterion function is calculated. Iteratively, cluster centers are updated and the process is ceased until the criterion function converges or the iteration number reaches the pre-set threshold. The experimental results show that the proposed algorithm HIDK-means is effective in clustering heterogeneous objects and also scalable to large datasets.

关键词： Clustering algorithms

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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： Closed frequent item sets Data streams Sliding window Top-K

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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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A clustering algorithm for data stream based on spatial directed graph with core

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Journal of Convergence Information technology 2012年第19期7卷 131-139页

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

Due to the randomness of the partition of grids, the edge points of clusters might be partitioned into the sparse grids. These points would become noise information out of clusters when we cluster data stream by grid-density based algorithm. A data stream clustering algorithm based on spatial directed graph with core, SDGCStream, is proposed. It uses the spatial directed graph and the orthocenter of the sparse grids to handle the edge points of clusters. At first, the algorithm defines a structure SDGC (Spatial Directed Graph with Core) to store the summary statistics of data stream. The vertices of SDGC are maintained as the stream arriving. When the clustering quest comes, the edge information is generated. The initial clustering results are got through clustering on SDGC, then we judge whether the points of sparse grids which are adjacent to the border of a cluster belong to the cluster according to the orthocenter information and the border vertices of SDGC. At last, a strategy based on the distance between clusters is presented to adjust the clustering results after handling the border of clusters. The experimental results on synthetic and real datasets show the better validity of SDGCStream on handling the edge data points of clusters, and the scalability as the increasing of the length and dimensions of data stream.

关键词： Clustering algorithms

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Point cloud coarse registration based on 3DSIFT keypoint detection

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Journal of Computational Information systems 2014年第23期10卷 10121-10128页

作者： Gu, Xiaoying Liu, Yongshan Department of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Yanshan University Qinhuangdao China Data Industry Research Lab Qinhuangdao China

Since the SIFT feature point extraction algorithm with scale changes, rotation transformation invariance, is widely used in image registration. In this paper, the SIFT algorithm is applied to three-dimensional point cloud coarse registration, the proposed 3DSIFT extraction algorithm is suitable for three-dimensional point cloud data, then point coordinates, curvature, the nearest neighbor distance mean and other information compose fourteen-dimensional vector to conduct correspondence match, use the interior point rate of Ransac to obtain optimal transformation, and finally transform the coordinates for source point clouds using the optimal transform, complete the point cloud data coarse registration. Experimental results show that our coarse registration algorithm can effectively extract feature points, and it is robust for the point cloud with noisy point, it can provide accurate and effective initial value for the precise registration such as ICP.

关键词： Metadata

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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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Mining weighted closed sequential patterns by memory indexing

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2012年第19期6卷 432-440页

作者： Ren, Jiadong Li, Shuo 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

There exist two major problems in weighted closed sequential patterns mining. The first is that only the weights of items are considered and they ignore the time-interval information of data elements during the mining process;the second is that the existing weighted closed sequential pattern mining algorithms need to scan the sequence database many times or to construct numerous intermediate databases. To address these problems, we propose a memory-based algorithm, MIWCSpan (Memory Indexing for Weighted Closed Sequential pattern mining), for weighted closed sequential pattern mining. In the algorithm, we define a novel sequence weighting approach to find more interesting sequential patterns. Both the weight of sequence items and the time-interval of the data elements are considered in this approach. Moreover, an improved index set based on time-interval, p-iidx, is defined. The structure is a set of triples which store the pointer pointing to the sequence containing p, the time-interval of p in the sequence and the position where p occurs. In the mining process, MIWCSpan first scans the sequence database to read the database into memory. Then it adopts the find-then-index technique recursively to find the items which can constitute a weighted closed sequential pattern and construct p-iidx for the possible weighted closed sequential pattern. At last, the algorithm uses the close-detecting to mine the weighted closed sequential patterns efficiently. The experimental results show that MIWCSpan is better on running time, and it has good scalability.

关键词： Database systems

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Subspace clustering based on finding effective spaces

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 580-589页

作者： Ren, Jiadong Liu, Zhigang Dong, Jun 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

In high-dimensional data space, because the data is sparse inherently, clusters tend to exist in different subspaces, which makes the traditional methods no longer suitable for use. In this paper, we present SCFES, a subspace clustering algorithm based on finding effective spaces. First, we define the effective dimension. By calculating relative entropy we remove redundancy dimensions which affect clustering accuracy. Second, according to the data distribution in the effective dimensions, we get the effective intervals through merging adjacent intervals. The effective space is composed of effective intervals. Third, we extend the density estimator based on undirected acyclic connected graph by using weight so as to estimate the expectation of existing clusters in the space, at the same time combine it with the monotonicity of the clustering criterion mentioned in the CLIQUE algorithm to prune candidates. Consequently we get the effective spaces. Finally, we adopt the structure of sibling tree to store all the effective spaces and use DBSCAN algorithm based on density to generate maximal subspace clusters in some effective spaces. Experimental results show that SCFES effectively finds arbitrarily shaped and positioned clusters in different subspaces. Meanwhile SCFES has better clustering quality and scalability.

关键词： Clustering algorithms

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Method of periodic dynamic pattern mining based on complex network

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Journal of Computational Information systems 2015年第21期11卷 7849-7856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

Traditionally, analysis of dynamic network has been focused only on a single snapshot or integrated network obtained over a period of time. However, the temporal feature in dynamic network has been ignored. In this paper, periodic dynamic pattern (PDP) mining method is proposed to discover the dynamic evolution rule of social networks. We define a periodic dynamic pattern based on complex network, and construct matrix of graph (MG) to present the evolution of complex network over time. Moreover, a regular edge pattern searching (REPS) algorithm based on MG is designed. It is used to select frequent and regular edge existence sequence (EES) in MG with progressive scan. To improve efficiency of the algorithm, sequences according with frequency threshold and REP should be transformed to decimal integer. Then, depth-first search method is used to mine PDP, which owns the same REP in dynamic network. So, we can predict future network evolution and control its behavior. Experimental results demonstrate that our method is an effective periodic dynamic pattern mining. Copyright © 2015 Binary Information Press.

关键词： Complex networks

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VReading Multi-Sensory Reading Platform

VReading Multi-Sensory Reading Platform

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International Conference on virtual Reality and Visualization (ICVRV)

作者： Yuhan Liu Yong Tang Jiayin Song Jing Zhao Mengya Lv Qian Sun Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China Yanshan University College of Economics and Management Qinhuangdao China

ISBN: (纸本)9781538684986;9781538684979

The form of books is constantly developing with the upgrading of carrying media, and the emergence of electronic books has greatly shaken the traditional paper books. In recent years, with the combination of artificial intelligence, virtual reality, high-speed network and digital reading, the concept of "VR" has been applied to more and more industries. The introduction of VReading multi-sensory reading platform will bring new ideas to digital reading industry.

关键词： Conferences virtual reality Visualization

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