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IMSPMIS-Stream: incremental mining of top-k short sequential pattern over multiple item set streams

Journal of Computational information Systems 2014年第6期10卷 2305-2312页

作者： Hao, Xiaobing Han, Gaowei Chen, Yuping Wang, Peilong 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 previous algorithms in data streams are about single stream, which can only process single items. The algorithms about data streams are always extended by sequential pattern algorithms about static database, they can't satisfy the requirement of scanning streams only once and online mining. Moreover, top-k short sequential pattern hasn't been studied yet. Therefore, IMSPMIS-Stream algorithm is proposed to mine online multiple streams with the single scan by the incremental method in this paper. The streams are continuous transactions, which is also called item set streams. Several streams are mined interactively based on sliding windows at the same time. An approach to count the support of every sub item set is designed to get the support during one scan. The short sequential pattern is deffned, whose item sets are shorter than all the item sets with the same occurring time. When the arrival transactions reach to the capacity of the window, sequential patterns will be output by combining top-k short item sets. The experiments show that the IMSPMIS-Stream algorithm overcomes the memory limitation and its execution time is reduced compared with some previous algorithms under the same conditions. Copyright © 2014 Binary information Press.

关键词： information systems

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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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XGRouter: high-quality global router in X-architecture with particle swarm optimization

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Frontiers of computer Science 2015年

作者： Liu, Genggeng Guo, Wenzhong Li, Rongrong Niu, Yuzhen Chen, Guolong College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

This paper presents a high-quality very large scale integration (VLSI) global router in X-architecture, called XGRouter, that heavily relies on integer linear programming (ILP) techniques, partition strategy and particle swarm optimization (PSO). A new ILP formulation, which can achieve more uniform routing solution than other formulations and can be effectively solved by the proposed PSO is proposed. To effectively use the new ILP formulation, a partition strategy that decomposes a large-sized problem into some small-sized sub-problems is adopted and the routing region is extended progressively from the most congested region. In the post-processing stage of XGRouter, maze routing based on new routing edge cost is designed to further optimize the total wire length and mantain the congestion uniformity. To our best knowledge, XGRouter is the first work to use a concurrent algorithm to solve the global routing problem in X-architecture. Experimental results show that XGRouter can produce solutions of higher quality than other global routers. And, like several state-of-the-art global routers, XGRouter has no overflow. © 2015 Higher Education Press and Springer-Verlag Berlin Heidelberg

关键词： VLSI circuits

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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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Darwintree: A molecular data analysis and application environment for phylogenetic study

Darwintree: A molecular data analysis and application enviro...

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作者： Meng, Zhen Dong, Hui Li, Jianhui Chen, Zhiduan Zhou, Yuanchun Wang, Xuezhi Zhang, Shouzhou Scientific Data Center Computer Network Information Center Chinese Academy of Sciences 4 4th South Street Zhongguancun Haidian District Beijing100190 China Key Laboratory of Southern Subtropical Plant Diversity Fairylake Botanical Garden Shenzhen and Chinese Academy of Sciences 160 Xianhu Rd. Luohu District Shenzhen518004 China State Key Laboratory of Systematic and Evolutionary Botany Institute of Botany Chinese Academy of Sciences 20 Nanxincun Xiangshan Beijing100093 China

DarwinTree (http://***) provides an integrated bioinformatics platform that supports all phases of the analytical pathway for phylogenetic study from data collections, phylogenetic tree constructions, visualization of the tree of life, and web-based rendering to specific application services & data mining. First, it is a repository for sequence records that form the basic data unit of all phylogenetic studies. Second, it is a workbench that aids the management, quality assurance, and analysis of phylogenetic data. Third, it provides a community of phylogenetic application services with tree reconstruction and related data mining. This paper provides a brief introduction to the key elements of DarwinTree, discusses their functional capabilities, details the database features, phylogeny pipeline, data mining tools, and specific application services available to support it, and concludes with future prospects. © 2015 The Author(s).

关键词： Phylogenetic

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Defect inspection for TFT-LCD images based on the low-rank matrix reconstruction

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Neurocomputing 2015年 149卷 1206-1215页

作者： Yi-Gang Cen Rui-Zhen Zhao Li-Hui Cen Li-Hong Cui Zhen-Jiang Miao Zhe Wei School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing 100044 China School of Information Science and Engineering Central South University Changsha Hunan 410083 China Department of Mathematics Beijing University of Chemical Technology Beijing 100029 China Electrical and Computer Engineering McMaster University Ontario Canada

Surface defect inspection of TFT-LCD panels is a critical task in LCD manufacturing. In this paper, an automatic defect inspection method based on the low-rank matrix reconstruction is proposed. The textured background of the LCD image is a low-rank matrix and the foreground image with defects can be treated as a sparse matrix. By utilizing the Inexact Augmented Lagrange Multipliers (IALM) algorithm, the segmentation of a LCD image can be converted into the reconstruction of a low-rank matrix with a fraction of its entries arbitrarily corrupted. This low-rank matrix reconstruction problem can be exactly solved via convex optimization that minimizes a combination of the nuclear norm and the l 1 -norm. Also, adaptive parameter selection strategy is proposed by conducting deep analysis on the IALM algorithm, which improves the generality of the IALM algorithm for different defect types. Experiment results show that our inspection algorithm is robust for the defect shapes and types under different illumination conditions. The shapes and edges of defect areas in the LCD images can be well preserved and segmented from textured background by our detection algorithm.

关键词： Defect inspection Low-rank matrix reconstruction LCDs IALM Adaptive parameter selection

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Mining important nodes in directed-weighted software network based on semi-global structure information

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ICIC Express Letters 2015年第10期9卷 2859-2866页

作者： Ren, Jiadong Wu, Hongfei Yin, Tengteng Liu, Yang He, Haitao College of Information Science and Engineering Yanshan University No. 438 West Hebei Ave. Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 West Hebei Ave. Qinhuangdao China

Software’s control flow graph can be treated as a complex software network, and a tiny fraction of key function nodes has considerable influence on the stability, reliability and robustness of the network. Identifying the top-k key nodes accurately and effectively has become an important topic in software engineering. In this paper, directedweighted function dependency network (DWFDN) is adopted to represent the function and relationship of function calling or dependency. The weight on a direct edge indicates the frequency of function calling. Weighted semi-global structure information (the weight, the relative position of a function node and the topological structure among its neighbor nodes) is taken into consideration in our approach. From the perspective of accumulating and propagating vulnerabilities, key function callers and function callees are mined respectively. Case studies on real software date show the utility of the approach. © 2015, ICIC International.

关键词： Software reliability

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A feature selection algorithm based on Hoeffding inequality and mutual information

International Journal of Signal Processing, Image Processing...

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International Journal of Signal Processing, Image Processing and Pattern Recognition 2015年第11期8卷 433-444页

作者： Yin, Chunyong Feng, Lu Ma, Luyu Yin, Zhichao Wang, Jin School of Computer and Software Jiangsu Key Laboratory of Meteorological Observation and Information Processing Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China Nanjing No.1 Middle School Nanjing China

With the rapid development of the Internet, the application of data mining in the Internet is becoming more and more extensive. However, the data source’s complex feature redundancy leads that data mining process becomes very inefficient and complex. So feature selection research is essential to make data mining more efficient and simple. In this paper, we propose a new way to measure the correlation degree of internal features of dataset which is a mutation of mutual information. Additionally we also introduce Hoeffding inequality as constraint of constructing algorithm. During the experiments, we use C4.5 classification algorithm as test algorithm and compare HSF with BIF(feature selection algorithm based on mutual information). Experiments results show that HSF performances better than BIF[1] in TP and FP rate, what’s more the feature subset obtained by HSF can significantly improve the TP, FP and memory usage of C4.5 classification algorithm. © 2015 SERSC.

关键词： Feature Selection

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Friends recommendation with rating side information

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Open Cybernetics and Systemics Journal 2015年第1期9卷 807-815页

作者： Hu, Xiang Wang, Wengdong Gong, Xiangyang Wang, Bai Que, Xirong Xia, Hongke State Key Laboratory of Network and Switching Beijing University of Posts and Telecommunications Beijing100876 China School Of Control And Computer Engineering North China Electric Power University Beijing102206 China Computer School Beijing Information Science & Technology University Beijing100101 China

With the broad application of web 2.0 technology, various kinds of online social networks arise at present. The emergence of social networks not only helps the public to facilitate the sharing and communicating, but also helps them to know more new friends from cyberspace social circle. Therefore, friends recommendation becomes a critical function for various online social networks. Problem of friends recommendation is actually the problem of link prediction in essential, and vast majority of the solutions are traditionally based on the social relation, yet common interests are also the important factor of forming friendships in real life besides social relations. This article proposes a new matrix factorization based method for friends recommendation. The proposed method considers two data sources of social relations and interest ratings simultaneously, utilizes Gaussian kernel to capture interest relevance (interest similarity) between persons, makes use of Gaussian process to generate the users’ profile vectors, and eventually achieves a recommendation method having the capability of commending friends with common interests. Experiments show that our method outperforms the art-of-the-state traditional link prediction methods using only social relations. © Hu et al.

关键词： Social networking (online)

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