Exemplar-Based Clustering Analysis Optimized by Genetic Algorithm

Exemplar-based clustering algorithm is very efficient to handle large scale and high dimensional data, while it does not require the user to specify many pa- rameters. For current algorithms, however, are the inabil- ities to identify the optimal results or specify the number of clusters automatically. To remedy these, in this work, we propose and explore the idea of exemplar-based cluster- ing analysis optimized by genetic algorithms, abbreviated as ECGA framework, which use genetic algorithms for op- timizing and combining the results. First, an exemplar- based clustering framework based on canonical genetic al- gorithm is introduced. Then the framework is optimized with three new genetic operators： （1） Geometry operator which limits the typology distribution of exemplars based on pair-wise distances, （2） EM operator which apply EM （Expectation maximization） algorithm to generate children from previous population and （3） Vertex substitution op- erator which is initialized with genetic algorithm and se- lect exemplars by using the variable neighborhood search meta-heuristic framework. Theoretical analysis proves the ECGA can achieve better chance to find the optimal clus- tering results. Experimental results on several synthetic and real data sets show our ECGA provide comparable or better results at the cost of slightly longer CPU time.     

DATA PREPROCESSING AND RE KERNEL CLUSTERING FOR LETTER

Many classifiers and methods are proposed to deal with letter recognition problem. Among them, clustering is a widely used method. But only one time for clustering is not adequately. Here, we adopt data preprocessing and a re kernel clustering method to tackle the letter recognition problem. In order to validate effectiveness and efficiency of proposed method, we introduce re kernel clustering into Kernel Nearest Neighbor classification （KNN）, Radial Basis Function Neural Network （RBFNN）, and Support Vector Machine （SVM）. Furthermore, we compare the difference between re kernel clustering and one time kernel clustering which is denoted as kernel clustering for short. Experimental results validate that re kernel clustering forms fewer and more feasible kernels and attain higher classification accuracy.     

独特码及其编码原理研究

简要介绍了独特码基本概念，研究独特码应具备的性质并进行了相关证明，最后用实例验证了本文提出的独特码的分级编码方法。     

一种基于网格的等密度线聚类算法

提出了一种基于网格的等密度线聚类算法,通过对样本所在空间进行网格划分,从样本分布等密度线图的思想出发,可自动发现任何形状的类,时间复杂度和空间复杂度较好,实现了对不同类样本空间容积的计算,具有很好的聚类效果和容积计算能力.     

基于无人机巡线图像的地面油气管道识别方法

限于成本,无人机搭载的任务设备主要为普通数码相机,采集的是可见光图像,针对此种情况,提出了一种利用彩色分割及形状检测识别油气管道的方法,首先需要设定感兴趣区域ROI,计算出协方差矩阵C和均值m,并使用欧氏距离、马氏距离对图像进行彩色聚类分割,然后对分割图像填色后进行边缘检测,最后根据边缘图像进行霍夫变换来检测直线特征,实现复杂环境下对管道位置的自动定位.测试图像库包含300幅图像,识别准确率达到80.3%,实验结果表明,在色彩差异较大背景中,基于颜色和形状特征的识别方法能有效进行管线跟踪定位.     

基于数据融合的某型装备品质状态评估

基于品质状态评估的背景,在分析某型装备品质状态影响因素的基础上,从通用性能信息、测试信息、环境信息和装备履历信息出发,分别运用BP神经网络、ER算法、物元理论和灰色聚类法进行分析评估;最终进行各类信息的数据融合,从而掌握装备的品质状态,为装备品质管理与维修决策提供支持.     

模糊聚类法在水环境质量评价中的应用

模糊聚类法通过数据标准化、平移及极差变换、确定最佳阈值和求解动态聚类图.该算法在长江芜湖段水环境质量评价中的应用,以选取对长江芜湖段水质影响较大的7个指标并建立其模糊相似矩阵,根据实测浓度值确定各因子的权重,结果可行.     

一种改进的基于密度的聚类算法

聚类是数据挖掘领域中的一个重要研究方向,在基于密度的聚类算法DBSCAN的基础上,提出了一种改进的基于密度的聚类算法,该算法在核心点的邻域扩展中不再将邻域内的点作为种子点,而是按顺序选择一个邻域外未被标记的点作为种子点,然后分不同情况进行相应的聚类扩展,此算法可以有效减少聚类中核心点邻域重叠区域查询的次数和运行的时间,实验测试结果也表明该算法聚类的效率和质量明显优于DBSCAN算法.     

网络心跳包序列的数据流分簇检测方法

在对网络会话进行时序分析的基础上,提出基于数据流分簇处理的心跳包序列检测方法。对数据流进行时序分簇处理,按周期性特征扩充簇集合,筛除不符合特征的簇对象,根据稳定的簇集合检测心跳包序列。实验结果表明,该方法检测率较高、误检率较低,能够实现实时检测和处理。     

An unequal cluster-based routing protocol in wireless sensor networks

Clustering provides an effective method for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques; selecting cluster heads with more residual energy, and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spot problem in multihop sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavier relay traffic and tend to die much faster, leaving areas of the network uncovered and causing network partitions. To mitigate the hot spot problem, we propose an Unequal Cluster-based Routing (UCR) protocol. It groups the nodes into clusters of unequal sizes. Cluster heads closer to the base station have smaller cluster sizes than those farther from the base station, thus they can preserve some energy for the inter-cluster data forwarding. A greedy geographic and energy-aware routing protocol is designed for the inter-cluster communication, which considers the tradeoff between the energy cost of relay paths and the residual energy of relay nodes. Simulation results show that UCR mitigates the hot spot problem and achieves an obvious improvement on the network lifetime. Guihai Chen obtained his B.S. degree from Nanjing University, M. Engineering from Southeast University, and PhD from University of Hong Kong. He visited Kyushu Institute of Technology, Japan in 1998 as a research fellow, and University of Queensland, Australia in 2000 as a visiting professor. During September 2001 to August 2003, he was a visiting professor at Wayne State University. He is now a full professor and deputy chair of Department of Computer Science, Nanjing University. Prof. Chen has published more than 100 papers in peer-reviewed journals and refereed conference proceedings in the areas of wireless sensor networks, high-performance computer architecture, peer-to-peer computing and performance evaluation. He has also served on technical program committees of numerous international conferences. He is a member of the IEEE Computer Society. Chengfa Li was born 1981 and obtained his Bachelor’s Degree in mathematics in 2003 and his Masters Degree in computer science in 2006, both from Nanjing University, China. He is now a system programmer at Lucent Technologies Nanjing Telecommunication Corporation. His research interests include wireless ad hoc and sensor networks. Mao Ye was born in 1981 and obtained his Bachelor’s Degree in computer science from Nanjing University, China, in 2004. He served as a research assistant At City University of Hong Kong from September 2005 to August 2006. He is now a PhD candidate with research interests in wireless networks, mobile computing, and distributed systems. Jie Wu is a professor in the Department of Computer Science and Engineering at Florida Atlantic University. He has published more than 300 papers in various journal and conference proceedings. His research interests are in the areas of mobile computing, routing protocols, fault-tolerant computing, and interconnection networks. Dr. Wu serves as an associate editor for the IEEE Transactions on Parallel and Distributed Systems and several other international journals. He served as an IEEE Computer Society Distinguished Visitor and is currently the chair of the IEEE Technical Committee on Distributed Processing (TCDP). He is a member of the ACM, a senior member of the IEEE, and a member of the IEEE Computer Society.