机群环境下傅立叶变换的并行算法研究

并行FFT是解决大数据量FFT运算耗时过久的重要途径,在PC机群上实现并行FFT是一种低成本、高效率的解决方案。本文讨论了PC机群环境下MPI并行FFT实现,并利用建立的平台,对并行算法进行了测试,得出了一些有意义的结论和方法。     

基于聚类矩阵的入侵日志关联规则算法

分析已有的关联规则算法,提出一种基于聚类矩阵的入侵检测日志关联规则算法。当数据库和最小支持度发生变化时,只需扫描变动的数据即可得到新的频繁项集。实验结果表明,该算法只需扫描一次数据库,具有频繁k-项集生成速度快、节约时间等优点,能提高入侵检测日志数据库关联规则挖掘的效率,满足实时入侵检测系统的需要。     

关于数据挖掘中聚类分析的研究进展

聚类分析是数据挖掘的一种重要技术,在本文中,回顾了几种现有的聚类分析的方法,指出了这些方法的优劣并且总结了聚类分析的主要研究方向并对聚类分析进行了前景展望。     

Cluster partition-based communication of multiagents: The model and analyses

In previous works, the agent communication always takes place randomly through the whole agents distributed area, and all agents are treated equally in the communication process. However, the real communication is always imbalanced: communication in some places of the system may be very busy and the communication resources are insufficient, whereas in other places the communication may be very idle and the communication resources are redundant. To make up the shortage of related works, this paper presents a cluster partition-based communication model for multiagents. In the presented model, agents are divided into some clusters according to their communication frequencies; in each cluster, one agent is set to be hub-agent which carries the information of other agents within the cluster. With the presented model, the communication costs within the same cluster is lower than the one among different clusters because agents have closer distances within the same cluster; moreover, many real communication processes may take place within clusters since the clusters are partitioned according to the communication frequencies among agents. Therefore, the communication time and costs can be reduced by adopting our model. Finally, the advantage of our model is proved by a series of experiments.     

Ab initio calculation of the BCC Fe–Al–Mo (Iron–Aluminum–Molybdenum) phase diagram: Implications for the nature of the phase

The metastable phase diagram of the BCC-based ordering equilibria in the Fe–Al–Mo system has been calculated via a truncated cluster expansion, through the combination of Full-Potential-Linear augmented Plane Wave (FP-LAPW) electronic structure calculations and of Cluster Variation Method (CVM) thermodynamic calculations in the irregular tetrahedron approximation. Four isothermal sections at 1750 K, 2000 K, 2250 K and 2500 K are calculated and correlated with recently published experimental data on the system. The results confirm that the critical temperature for the order–disorder equilibrium between Fe₃Al–D0₃ and FeAl–B2 is increased by Mo additions, while the critical temperature for the FeAl–B2/A2 equilibrium is kept approximately invariant with increasing Mo contents. The stabilization of the Al-rich A2 phase in equilibrium with overstoichiometric B2–(Fe,Mo)Al is also consistent with the attribution of the A2 structure to the τ₂ phase, stable at high temperatures in overstoichiometric B2–FeAl.     

First-principles calculation of L10 -disorder phase equilibria for Fe–Ni system

By combining Cluster Variation Method with FLAPW electronic structure total energy calculations and the Debye–Grüneisen theory within quasi-harmonic approximation, L1₀-disorder phase equilibria for Fe–Ni system are calculated. The transition temperature, 483 K, determined in the present calculation is lower than that obtained in the previous calculation without thermal vibration effects. The decrease of the transition temperature is ascribed to the enhanced phase stability of a disordered phase due to the thermal softening of a lattice.     

A fuzzy modeling method via Enhanced Objective Cluster Analysis for designing TSK model

This paper proposes a fuzzy modeling method via Enhanced Objective Cluster Analysis to obtain the compact and robust approximate TSK fuzzy model. In our approach, the Objective Cluster Analysis algorithm is introduced. In order to obtain more compact and more robust fuzzy rule prototypes, this algorithm is enhanced by introducing the Relative Dissimilarity Measure and the new consistency criterion to represent the similarity degree between the clusters. By these additional criteria, the redundant clusters caused by iterations are avoided; the subjective influence from human judgment for clustering is weakened. Moreover the clustering results including the number of clusters and the cluster centers are considered as the initial condition of the premise parameters identification. Thus the traditional iteration modeling procedure for determining the number of rules and identifying parameters is changed into one-off modeling, which significantly reduces the burden of computation. Furthermore the decomposition errors and the approximation errors resulted from premise parameters identification by Fuzzy c-Means clustering are decreased. For the consequence parameters identification, the Stable Kalman Filter algorithm is adopted. The performance of the proposed modeling method is evaluated by the example of Box–Jenkins gas furnace. The simulation results demonstrate the power of our model.     

一种面向非平衡数据的多簇IB算法

信息瓶颈(Information Bottleneck,IB)方法在处理非平衡数据集时,倾向于将大簇中的数据对象划分到数据规模较小的小簇中,造成了聚类效果不理想的问题。针对该问题,提出了一种面向非平衡数据的多簇信息瓶颈算法(McIB)。McIB算法采用向下抽样方法来降低非平衡数据集的倾斜度,使用先划分再学习后合并的策略来优化IB算法处理非平衡数据的合并抽取过程。整个算法包含3步:首先根据分离标准来确定抽样比例参数;然后对数据进行初步的聚类,生成可信赖的多个簇;最后再利用簇之间的相似性对簇进行合并,组织多个簇代表每个实际的簇来得到最终的聚类结果。实验结果表明:所提算法能够有效地解决IB方法在非平衡数据集上的“均匀效应”问题;与其他聚类算法相比,McIB算法的性能更优。     

Generalized Possibilistic Fuzzy C-Means with novel cluster validity indices for clustering noisy data

A generalized form of Possibilistic Fuzzy C-Means (PFCM) algorithm (GPFCM) is presented for clustering noisy data. A function of distance is used instead of the distance itself to damp noise contributions. It is shown that when the data are highly noisy, GPFCM finds accurate cluster centers but FCM (Fuzzy C-Means), PCM (Possibilistic C-Means), and PFCM algorithms fail. FCM, PCM, and PFCM yield inaccurate cluster centers when clusters are not of the same size or covariance norm is used, whereas GPFCM performs well for both of the cases even when the data are noisy. It is shown that generalized forms of FCM and PCM (GFCM and GPCM) are also more accurate than FCM and PCM. A measure is defined to evaluate performance of the clustering algorithms. It shows that average error of GPFCM and its simplified forms are about 80% smaller than those of FCM, PCM, and PFCM. However, GPFCM demands higher computational costs due to nonlinear updating equations. Three cluster validity indices are introduced to determine number of clusters in clean and noisy datasets. One of them considers compactness of the clusters; the other considers separation of the clusters, and the third one considers both separation and compactness. Performance of these indices is confirmed to be satisfactory using various examples of noisy datasets.