无线传感器网络中基于树的动态多路径容错路由协议

多路径路由对无线传感器网络的负载均衡和容错能力等方面都有一定改善作用。在研究一些多路径路由算法的基础上,对分层的无线传感器网络路由进行探索,提出了一种基于树的动态多路径路由的生成方法,快速获得当前节点的主路径与备用路径。仿真结果表明,该方法可有效地均衡网络负载、减少时延、提高网络的可靠性。     

Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area

This study aims to provide a rapid screening tool for assessment of sustainable flood retention basins (SFRBs) to predict corresponding dam failure risks. A rapid expert-based assessment method for dam failure of SFRB supported by an artificial neural network (ANN) model has been presented. Flood storage was assessed for 110 SFRB and the corresponding Dam Failure Risk was evaluated for all dams across the wider Greater Manchester study area. The results show that Dam Failure Risk can be estimated by using the variables Dam Height, Dam Length, Maximum Flood Water Volume, Flood Water Surface Area, Mean Annual Rainfall (based on Met Office data), Altitude, Catchment Size, Urban Catchment Proportion, Forest Catchment Proportion and Managed Maximum Flood Water Volume. A cross-validation R² value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets. Traditionally, dams are considered safe because they have been built according to high technical standards. However, many dams that were constructed decades ago do not meet the current state-of-the-art dam design guidelines. Spatial distribution maps show that dam failure risks of SFRB located near cities are higher than those situated in rural locations. The proposed tool could be used as an early warning system in times of heavy rainfall.     

Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs

Clustering of retail stores in a distribution network with specific geographical limits plays an important and effective role in distribution and transportation costs reduction. In this paper, the relevant data and information for an established automotive spare-parts distribution and after-sales services company (ISACO) for a 3-year period have been analyzed. With respect to the diversity and lot size of the available information such as stores location, order, goods, transportation vehicles and road and traffic information, three effecting factors with specific weights have been defined for the similarity function: 1. Euclidean distance, 2. Lot size 3. Order concurrency. Based on these three factors, the similarity function has been examined through 5 steps using the Association Rules principles, where the clustering of the stores is performed using k-means algorithm and similar stores are allocated to the clusters. These steps include: 1. Similarity function based on the Euclidean distances, 2. Similarity function based on the order concurrency, 3. Similarity function based on the combination of the order concurrency and lot size, 4. Similarity function based on the combination of these three factors and 5. Improved similarity function. The above mentioned clustering operation for each 5 cases addressed in data mining have been carried out using R software and the improved combinational function has been chosen as the optimal clustering function. Then, trend of each retail store have been analyzed using the improved combinational function and along with determining the priority of the depot center establishment for every cluster, the appropriate distribution policies have been formulated for every cluster. The obtained results of this study indicate a significant cost reduction (32%) in automotive spare-parts distribution and transportation costs.     

Segmenting customers by transaction data with concept hierarchy

The segmentation of customers is crucial for an organization wishing to develop appropriate promotion strategies for different clusters. Clustering customers provides an in-depth understanding of their behavior. However, previous studies have paid little attention to the similarity of different items in transaction. Lack of categories and concept levels of items, results from item-based segmentation methods are not as good as expected. Through employing a concept hierarchy of items, this study proposes a segmentation methodology to identify similarities between customers. First, the dissimilarity between transaction sequences is defined. Second, we adopt hierarchical clustering method to segment customers by their transaction data with concept hierarchy of consumed items. After segmentation, three cluster validation indices are used for optimizing the number of clusters of customers. Through the compassion of normalized index, the segmentation method proposed by this study rendered better results than other traditional methods.     

SA-BFSN:一种自适应基于密度聚类的算法

针对BFSN算法需要人工输入参数r和λ的缺陷,提出了一种自适应确定r和λ的SA-BFSN聚类方法。该方法通过Inverse Gaussian拟合判断r参数,通过分析噪声点数量的分布特征选择合适的λ值。算法测试表明,使用SA-BFSN无需人工输入参数,能够实现聚类过程的全自动化,能够有效处理任意形状、大小和密度的簇。     

改进分块支持向量聚类在入侵检测中的应用

支持向量聚类(SVC)是在支持向量机的思想上发展而来一种聚类方法,针对其处理大规模数据集速度缓慢的缺点,提出了一种改进的分块支持向量聚类算法。改进的算法分为三个阶段:前期的预处理,中期的分块算法,后期的改进标类算法。提出的方法显著加快了SVC的速度,在保持原来SVC算法的优点的基础上,对大规模数据集以及非均匀分布数据集等具有良好的效果。将其应用到网络入侵检测,实验结果表明改进的算法行之有效。     

Robust matrix 𝒟U‐stability analysis

In this paper, the problem of robust matrix root‐clustering is addressed. The studied matrices are subject to both polytopic and unstructured uncertainties. An original point is the large choice of clustering regions enabled by the proposed approach since these regions can be unions of possibly disjoint and non‐symmetric subregions of the complex plane. The precise purpose is, considering a specified polytope, to determine the greatest robustness bound on the unstructured uncertainty such that robust matrix root‐clustering is ensured. To reduce conservatism in the derivation of the bound, the reasoning relies on a framework based upon parameter‐dependent Lyapunov functions. The bound value is computed by solving an ?? ?? ? problem. Copyright © 2003 John Wiley & Sons, Ltd.     

基于蚁群聚类算法的离群挖掘方法

离群挖掘是数据挖掘研究的重要内容,在实际生活中获得广泛应用。该文首先给出了离群数据的量化定义,并用基于蚁群的聚类学习方法,产生了状态空间的整体特征。然后结合具体的设备对象,提出了离群数据的挖掘方法。最后进行了实验验证,结果表明该文提出的方法是有效的。     

基于网格的参数自动化聚类算法

提出了一种基于网格的参数自动化聚类算法PAG，主要目的是解决传统的网格聚类算法对参数敏感的问题．算法采用参数自动化技术来处理参数，即算法开始运行时所需的参数直接由参数自动化技术中的公式计算得出，不需要用户输入任何参数．通过对大量数据集的实验表明，该算法可扩展性好，能处理任意形状和大小的聚类，能够很好地识别出孤立点或噪声，并且有很好的精度．     

基于离群点检测和自适应参数的三支DBSCAN算法

针对经典的DBSCAN算法存在难以确定全局最优参数和误判离群点的问题,该算法首先从选择最优参数角度出发,通过数据集的分布特征生成Eps和MinPts列表,将两个列表中的参数进行全组合操作,把不同的参数组合依次进行聚类,从而寻找准确率最高点对应的参数。最后从离群点角度出发,将三支决策思想与离群点检测LOF算法进行结合。该算法与多种聚类算法进行效果对比分析,结果表明该算法能够全自动化选择全局最优参数,并提高聚类算法的准确性。