Fast agglomerative clustering using information of k-nearest neighbors

In this paper, we develop a method to lower the computational complexity of pairwise nearest neighbor (PNN) algorithm. Our approach determines a set of candidate clusters being updated after each cluster merge. If the updating process is required for some of these clusters, k-nearest neighbors are found for them. The number of distance calculations for our method is O(N²), where N is the number of data points. To further reduce the computational complexity of the proposed algorithm, some available fast search approaches are used. Compared to available approaches, our proposed algorithm can reduce the computing time and number of distance calculations significantly. Compared to FPNN, our method can reduce the computing time by a factor of about 26.8 for the data set from a real image. Compared with PMLFPNN, our approach can reduce the computing time by a factor of about 3.8 for the same data set.     

CLUSTERING VERY LARGE DATA SETS USING A LOW MEMORY MATRIX FACTORED REPRESENTATION

A scalable method to cluster data sets too large to fit in memory is presented. This method does not depend on random subsampling, but does scan every individual data sample in a deterministic way. The original data are represented in factored form by the product of two matrices, one or both of which is very sparse. This factored form avoids the need to multiply together these two matrices by using a variant of the Principal Direction Divisive Partitioning (PDDP) algorithm which does not depend on computing the distances between the individual samples. The resulting clustering algorithm is Piecemeal PDDP (PMPDDP), in which the original data are broken up into sections which will fit into memory and clustered. The cluster centers are used to create approximations to the original data items, and each original data item is represented by a linear combination of these centers. We evaluate the performance of PMPDDP on three real data sets, and observe that the quality of the clusters of PMPDDP is comparable to PDDP for the data sets examined.     

BUILDING USER TASK SPACE BY MINING TEMPORALLY PROXIMATE DESKTOP ACTIONS

Awareness of the user's current context, that is, the task the user resides in, can allow for various ways of supporting their work. In this article we present a novel approach for mining user's regular tasks on the basis of temporal proximity of users’ desktop actions without user intervention in a completely automatic, unobtrusive, and unsupervised manner. The proposed method is based on time-based clustering of a user's desktop actions and, in contrast to previous approaches, it does not enforce fixed time constraints on the mined actions, and typical transition times, when they exist, emerge from the logged activity data for that specific user. The performance of our technique was evaluated on a large data set of 724 days of desktop work by five knowledge workers. The results showed that our approach performed very well and was able to cluster those temporally proximate user actions that commonly used fix time window techniques fail to deal with. Notably, the main improvement is in the recall of the tasks, where our approach recalled 10.18% more actions than the predefined time window technique.     

自行车CA模型的元胞长度动态修正

采用固定元胞单元尺寸建立自行车流元胞自动机(CA)模型会产生仿真偏差。为此，提出一种基于现场视频检测数据的元胞尺寸标定方法。通过统计分析车头间距、横向间距与车速的关系，设计一个动态修正元胞长度的函数。对比修正前后模型演化获得的宏观三参数值，可以发现，修正函数能较好地描述跟车距离与车速的动态关系，克服模型因采用定长度元胞单元而造成的面积密度与流率偏差。     

Dynamic and adaptive detection method for flooding in wireless sensor networks

In recent years, wireless sensor networks (WSNs) have attracted an increasing attention in several fields. However, WSNs must be treated with significant challenges in their design due to their special characteristics such as limited energy, processing power, and data storage that make the energy consumption saving a real challenge. Also, regarding their distributed deployment in open radio frequency and lack of physical security, these networks are vulnerable and exposed to several attacks: passive eavesdropping, active attacks, and identity theft. In this paper, we propose a new method called accordion method to detect and apprehend denial of service attacks in WSNs. This approach is a dynamic and an adaptive method based on using clustering method which allows electing control nodes that analyze the traffic inside a cluster and send warnings to the cluster head whenever an abnormal behavior is suspected or detected. The proposed method relies on the analysis of the evolution of the threshold messages (alerts) sent in the cluster. The proposed method has been evaluated, and the obtained numerical results show its benefit compared with other detection methods.     

我国主要城市经济发展水平研究

运用主成分分析法对国内9个主要城市的经济发展水平进行了评价,得出了各城市的综合排名结果,并结合聚类分析对各城市经济发展水平进行分类及探讨,依据分析结果,提出了相应的城市建设建议。     

基于谱系聚类法的用户击键特征识别

以用户击键特征为依据，提出了一种基于谱系聚类法的识别算法。该算法通过谱系聚类法对用户击键特征向量进行聚类分析，并形成各向量之间的谱系关系，从而由谱系关系来对击键特征向量进行识别。该算法的主要特点是使用欧氏距离进行分类，算法实现简单并且识别速度快。由于采用的聚类算法的简单性，其识别精度尚有待提高，因此该算法适用于击键识别的简单应用。     

Impact of blasting parameters on vibration signal spectrum: Determination and statistical evidence

Research on the impact of blasting parameters on vibration signals is of significant value for guiding blast-resistant design. Previous research was primarily aimed at the impact on vibration amplitude in the time domain but rarely focused on energy distribution in the frequency domain (i.e., spectrum). Based on large amounts of blast signals from a series of events, in this study, the primary parameters that affect the vibration spectrum were determined. First, the K-means method was used to cluster all of the signals into six distinct spectrum clusters. The T test was then utilized among different clusters, to detect discrepancies in continuous parameters, including total charge, maximum charge, concrete age and distance between the explosion source and measuring point. Meanwhile, a random clustering simulation was conducted to determine whether the other two discrete parameters, i.e., the number of detonator relays and the explosion source location, influence the spectrums of vibration signals. The results show that three of the six parameters studied have a close link to the vibration spectrum, whereas the other three do not. This study also discusses how the parameters impact the occurrence and evolution of vibration signals.     

应用模糊聚类对50种小麦粉质与面包烘焙品质间关系初探

本文在粮食科学中使用模糊聚类方法,初步探讨了小麦粉质与面包烘焙品质间的内在联系。文中将面粉常用测试指标体系划分为同步、交叉、简约三种类型,讨论了优化代表性指标体系的原则,提出了几种优选面包用小麦品种的方法。