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

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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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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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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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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New methods of constructing quaternary sequence Pairs

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ICIC Express Letters 2016年第9期10卷 2177-2183页

作者： Jia, Yanguo Shen, Xiumin Duan, Xiaobei Song, Xiaofei School of Information Sciences and Engineering Yanshan University China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province No. 438 Hebei Ave. Qinhuangdao066004 China

In this letter, new methods of constructing quaternary sequence pairs are presented based on binary sequence pairs with two-level autocorrelation, almost perfect binary sequence pairs and cyclic shift sequences. The quaternary sequence pairs con- structed by these methods are periodic autocorrelation signals. These methods are handy and exible, and can greatly enrich the theory of quaternary sequence pairs. © 2016 ICIC International.

关键词： Autocorrelation

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Robot Aided Intelligent Kitchen Assistance system Using Eye Tracking Based on Object Recognition

Robot Aided Intelligent Kitchen Assistance System Using Eye ...

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2022 International Symposium on Design Studies and Intelligence Engineering, DSIE 2022

作者： Guan, Junhui Liu, Zheng Zhao, Xiaodong Cheng, Shiwei Zhejiang University of Technology School of Computer Science Hangzhou310023 China Zhejiang Provincial Key Laboratory of Integration of Healthy Smart Kitchen System Ningbo315336 China China Academy of Art Hangzhou310000 China

ISBN: (纸本)9781643683720

How to get the potential interaction intent is the key to design a personalized intelligent kitchen assistance system based on Human Robot Interaction (HRI), which can facilitate the elder people in their daily life. Eye movement includes abundant user intent information, eye tracking technology can be used to obtain and analyze this information, thus identifying user's potential intent. This paper proposed an eye tracking based object recognition method, which could recognize objects that the user was watching. Firstly, multiple fixation points were clustered into a fixation point that best represented the user's intent by research on the study of the clustering algorithm. Then the object recognition algorithm based on PP-ShiTu platform was used to obtain the specific location of the object in the scene image. Finally, this paper proposed a fixation based object recognition module, and used the module to design and implement a personalized intelligent kitchen assistance system, and verified the effectiveness through experiments. The experimental results showed that when there was only single food recognition, the average recognition accuracy of the personalized intelligent kitchen assistance system reached over 80%, and the average time delay of the system was about 1400 ms. © 2023 The authors and IOS Press.

关键词： Eye tracking

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A Generalized Weighted Closed Sequential Pattern Mining Algorithm with Item Interval

A Generalized Weighted Closed Sequential Pattern Mining Algo...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Haitao Lu Shuo Li College of Information Science and Engineering Yanshan University The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Library of Yanshan University Yanshan University

ISBN: (纸本)9781510803084

The algorithm of this paper inserts pseudo items which are converted from item interval to obtain equal extended sequence database;it defines item-interval constraints, which are relative to the item weight, to prune the mining patterns. Through doing this, the algorithm avoids mining the patterns which users are not interested in and shortens the running time. It adopts histogram statistic pattern to get the standardization description to item interval of the mining patterns, making the mining sequences include the item interval information which is valuable to user decision.

关键词： weighted sequential patterns closed sequential patterns item interval

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Algorithm for Acquiring Lettuce Plant Height Based on Image Recognition Network 4

Algorithm for Acquiring Lettuce Plant Height Based on Image ...

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4th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Guo, Xiyue Zhong, Yong Zhao, Ming Zhang, Man Wang, Minjuan Key Laboratory of Modern Precision Agriculture System Integration Research Ministry of Education China Agricultural University China Yantai Institute of China Agricultural University Yantai264670 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University China College of Information and Electrical Engineering China Agricultural University China

ISBN: (纸本)9781510666412

Lettuce height is one of the important phenotypic parameters of lettuce. Currently, evaluating plant height information based on RGB images is the most convenient and fastest method. However, these tasks often require complex camera calibration operations or reference objects with known heights, which are somewhat limiting. Therefore, we utilize a deep neural network to solve this problem to directly obtain the height of lettuce from RGB images without camera calibration and reference objects. This paper integrates four high-performance image recognition networks MobileNetV1, Densenet-121, ResNext-50, and EfficientNet-B3 using average fusion and linear fusion methods to predict lettuce plant height. Experiments show that we have achieved good results, the accuracy of the four basic models has been improved, and the average absolute error has been reduced to varying degrees. The smallest reduction is Efficient-Net, which has decreased 0.01 mm, the largest drop is the ResNext-50, which is 0.15mm lower. After the model is averagely fused, the average absolute error is further reduced to 1.22 mm, and the maximum absolute error is reduced to 6.48 mm, which is not only better than linear. The fusion results are better than the prediction results of a single model. © 2023 SPIE.

关键词： Lettuce

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