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.     

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction

This paper introduces and evaluates a new class of knowledge model, the recursive Bayesian multinet (RBMN), which encodes the joint probability distribution of a given database. RBMNs extend Bayesian networks (BNs) as well as partitional clustering systems. Briefly, a RBMN is a decision tree with component BNs at the leaves. A RBMN is learnt using a greedy, heuristic approach akin to that used by many supervised decision tree learners, but where BNs are learnt at leaves using constructive induction. A key idea is to treat expected data as real data. This allows us to complete the database and to take advantage of a closed form for the marginal likelihood of the expected complete data that factorizes into separate marginal likelihoods for each family (a node and its parents). Our approach is evaluated on synthetic and real-world databases.     

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.     

Capacitated Clustering Problems by Hybrid Simulated Annealing and Tabu Search

The capacitated clustering problem (CCP) is the problem in which a given set of weighted objects is to be partitioned into clusters so that the total weight of objects in each cluster is less than a given value (cluster ‘capacity’). The objective is to minimize the total scatter of objects from the ‘centre’ of the cluster to which they have been allocated. A simple constructive heuristic, a R-interchange generation mechanism, a hybrid simulated annealing (SA) and tabu search (TS) algorithm which has computationally desirable features using a new non-monotonic cooling schedule, are developed. A classification of the existing SA cooling schedules is presented. The effects on the final solution quality of the initial solutions, the cooling schedule parameters and the neighbourhood search strategies are investigated. Computational results on randomly generated problems with size ranging from 50 to 100 customers indicate that the hybrid SA/TS algorithm out-performs previous simulated annealing algorithms, a simple tabu search and local descent algorithms.     

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。     

模糊C-球壳聚类算法及其在血液细胞图像中的应用

文章从经典的模糊C均值算法开始通过改变其中相似性的度量形式，介绍了一种模糊C球壳聚类（FCSS）算法。在将该算法应用于细胞显微图像半径统计时，采用基于形态学的图像预处理措施，可以获得FCSS算法中有关原型模式的知识，加快收敛速度并避免随机初始化造成的局部极小问题。     

Analysis and visualisation with host-based software – The features of STNAnaVist™

This article is based on experiences with data and text mining to gain information for strategic business decisions, using host-based analysis and visualisation (A/V), primarily in the field of patents. The potential advantages of host-based A/V are pointed out and the features of the first such A/V software, STN^®AnaVist™, are described in detail. Areas covered include the user interfaces, initial set of documents for A/V, data mining, text mining, reporting, and suggestions for further development.     

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.     

一种基于Web文本聚类的用户兴趣发现模型的研究

研究了一种基于Web文本聚类的用户兴趣发现方法．他通过Web文档信息获取，文本的形式表示，以及Web文本聚类方法最终提取用户兴趣知识，并给出了一个设计模型。