Non-redundant data clustering

Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. In practice, this discovery process should avoid redundancies with existing knowledge about class structures or groupings, and reveal novel, previously unknown aspects of the data. In order to deal with this problem, we present an extension of the information bottleneck framework, called coordinated conditional information bottleneck, which takes negative relevance information into account by maximizing a conditional mutual information score subject to constraints. Algorithmically, one can apply an alternating optimization scheme that can be used in conjunction with different types of numeric and non-numeric attributes. We discuss extensions of the technique to the tasks of semi-supervised classification and enumeration of successive non-redundant clusterings. We present experimental results for applications in text mining and computer vision.     

Question hard, answer simply: A comment on Storms et al. (2003).

It seems clear that, for whatever reasons, the dementia of the Alzheimer type patient group (as well as other patient groups) exhibits behavior that is different from the normal control group. G. Storms, T. Dirikx, J. Saerens, S. Verstraeten, and P. P. De Deyn (2003) rightfully argue that the observed behavior (similarity judgments) does not tell us the source (cause) of the differences between the 2 groups. Rather, the focus of the study should be placed more on finding the ways the 2 groups are different. They also point out various methodological problems in some of the previous attempts to characterize the nature of the differences. Further methodological issues in G. Storms et al.'s study are examined. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

一种分布式的洋葱路由新机制

洋葱路由技术在公网上提供双向、实时的匿名通信，其面向连接和严格的源路由方式导致效率低、扩展性差。利用分布式的概念，采用分段连接技术思想提出了一种安全、高效的分布式洋葱路由机制(DORS)，给出了DORS路由实现算法，并从方案实现效率、安全性方面对DORS进行了分析。分析结果表明DORS具有良好的扩展性、鲁棒性、简单性和有效性。     

一种应用于二次布局的有效划分方法

提出了一种基于二次布局的结合MFFC结群和hMETIS划分的算法.实验表明:这种方法能得到很好的布局结果,但是运行消耗的时间比较长.为了缩短划分在二次布局中运行的时间,提出了一种改进的结群算法IMFFC,用它在二次布局中做划分.与前者相比较,这种方法虽然布局质量稍差,但速度更快.     

模糊聚类在三维地震参数处理中的应用

三维地震参数是一个庞大的三维数据体，能从不同侧面反映储集层的特征。通过对三维地震参数的分析，可以评估油藏表征的参数（沉积相、构造、生长历史、流体饱和度等）。针对传统的聚类方法不能很好地综合考虑各个参数之间的相似程度，提出了用模糊C-均值聚类（fuzzy c-means clustering，简称FCM）对三维地震参数进行处理。从三维地震参数和油藏表征参数之间的关系出发，采用模糊C-均值聚类方法对三维地震参数进行处理，依此评估油藏表征的参数。测试结果表明，模糊C-均值聚类方法能够对三维地震参数进行较为准确的分类，并为储集层的研究提供了很好的依据。     

A Unified View on Clustering Binary Data

Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This paper studies the problem of clustering binary data. Binary data have been occupying a special place in the domain of data analysis. A unified view of binary data clustering is presented by examining the connections among various clustering criteria. Experimental studies are conducted to empirically verify the relationships.     

Studies of substrate specificities of lipases from different sources

Although lipases are widely applied in a wide variety of reactions, available information on the factors that are responsible for the substrate specificities of lipases from different sources is scarce. In this paper, nine lipase‐producing microorganism strains were isolated from oil‐containing samples. The properties of these enzymes, including pH optima, temperature optima, thermal stability, and pH stability, vary significantly with the different sources from which these lipases were isolated. The substrate specificities of the nine lipases, including fatty acid and positional specificities, were also studied. The fatty acid specificities of the nine lipases were observably different toward 15 substrates with different carbon chain lengths, different saturation degrees and different side chains. The lipases from S₃ Penicillium citrinum (PCL), MJ₁ Aspergillus niger (ANL), MJ₂ Aspergillus oryzae (AOL), YM Bacillus coughing (BCL), S₉ Geotrichum candidum (GCL), and S₁₁ Candida lypolytica (CLL) showed the strongest specificities to short‐chain esters, and the other lipases showed strong selectivity for medium‐ or long‐chain and branched esters. The positional specificities were examined by analyzing the hydrolytic products of triolein catalyzed by the nine lipases, using TLC. The lipases can be mainly classified into two groups by their specificities for the ester bond at position 2 of triglycerides; one group can selectively hydrolyze the ester bond at position 2 of the triglycerides, the other group cannot. All these nine lipases were divided into four groups by hierarchical clustering analysis on the basis of the results of fatty acid and positional specificities.     

Optimal implementations of UPGMA and other common clustering algorithms

In this work we consider hierarchical clustering algorithms, such as UPGMA, which follow the closest-pair joining scheme. We survey optimal O(n²)-time implementations of such algorithms which use a ‘locally closest’ joining scheme, and specify conditions under which this relaxed joining scheme is equivalent to the original one (i.e. ‘globally closest’).     

Concealed weapon detection and visualization in a synthesized image

Images acquired by heterogeneous image sensors may provide complementary information about the scene, for instance, the visual image can provide personal identification information like the facial pattern while the infrared (IR) or millimeter wave image can detect the suspicious regions of concealed weapons. Usually, a technique, namely multiresolution pixel-level image fusion is applied to integrate the information from multi-sensor images. However, when the images are significantly different, the performance of the multiresolution fusion algorithms is not always satisfactory. In this study, a new strategy consisting of two steps is proposed. The first step is to use an unsupervised fuzzy k-means clustering to detect the concealed weapon from the IR image. The detected region is embedded in the visual image in the second step and this process is implemented with a multiresolution mosaic technique. Therefore, the synthesized image retains the quality comparable to the visual image while the region of the concealed weapon is highlighted and enhanced. The experimental results indicate the efficiency of the proposed approach.This material is based on part of the work carried out at the SPCR laboratory of Lehigh University and the work is partially supported by the U. S. Army Research Office under grant number DAAD19-00-1-0431. The content of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement should be inferred.     

Robust pole clustering in a good ride quality region of aircraft for matrices with structured uncertainties

This paper presents a general analysis of robust pole clustering in a good ride quality region (GRQR) of aircraft for matrices with structured uncertainties. This region is an intersection of a ring and a horizontal strip, located in the left half-plane, which is a specific non-Ω-transformable region providing good ride quality of aircraft. The paper applies the Rayleigh principle along the norm theory to analyze robust pole clustering within this region since the generalized Lyapunov theory is not valid for non-Ω-transformable regions. Concerned uncertainties are structured/parametric uncertainties, including interval matrices. The results are useful for robust control analysis and design, especially, of robust good ride quality of aircraft, shuttles, vehicles and space station, as well as some industrial systems. An example of the F-16 dynamics for which GRQR is suitable is included to illustrate the results.