An examination of the effect of six types of error perturbation on fifteen clustering algorithms

An evaluation of several clustering methods was conducted. Artificial clusters which exhibited the properties of internal cohesion and external isolation were constructed. The true cluster structure was subsequently hidden by six types of error-perturbation. The results indicated that the hierarchical methods were differentially sensitive to the type of error perturbation. In addition, generally poor recovery performance was obtained when random seed points were used to start theK-means algorithms. However, two alternative starting procedures for the nonhierarchical methods produced greatly enhanced cluster recovery and were found to be robust with respect to all of the types of error examined.     

Generating interior search directions for multiobjective linear programming

A new multiobjective linear programming (MOLP) algorithm is presented. The algorithm uses a variant of Karmarkar's interior-point algorithm known as the affine-scaling primal algorithm. Using this single-objective algorithm, interior search directions are generated and used to provide an approximation to the gradient of the (implicitly known) utility function. The approximation is guided by assessing locally relevant preference information for the various interior directions through interaction with a decision maker (DM). The resulting algorithm is an interactive approach that makes its progress towards the solution through the interior of the constraints polytope.     

The structure of “communities” and communications in the new millennium

The three lineages of information,—the genetic, the cultural and artifactual—will increasingly merge their constituent information contents through advances in biotechnology and information technology. This will redefine what constitutes “social” and what constitutes “community.” A community's members communicate with their “significant others” and change their internal information states (and their internal and external behaviors). Under conditions of merging, information exchanges occur across all the three lineages. In this sense, the concept of significant other,—that is, a communicating entity—, is now spread from human communities to encompass also the biological and the artifactual. A seamless merging between the three realms now occurs affecting their respective internal information stores. The resulting image of interactions that now arises is of multiple oceans of communities, operating at different levels, the genetic, the cultural and the artifactual. There are exchanges across the different levels, up and down and sideways, as information is translated from one realm to the other. These dynamics result in changes in the evolutionary characteristics of each lineage and sub‐lineage, including the internal perceptions from within a lineage, namely in the language of evolutionary epistemology, its “meanings” and “hypotheses” on the world. A future sociology must necessarily take into account these factors and incorporate the dynamics of all three realms.     

Bayesian Factor Analysis as a Variable-Selection Problem: Alternative Priors and Consequences

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor-loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, Muthén & Asparouhov proposed a Bayesian structural equation modeling (BSEM) approach to explore the presence of cross loadings in CFA models. We show that the issue of determining factor-loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov's approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike-and-slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set is used to demonstrate our approach.     

基于遗传算法的模糊综合评价在心理测量中的应用

提出了运用模糊数学对利克特量表数据进行分析的方法, 探讨了人们在进行模糊综合评价时, 所采用的算子和对各个自变量的权重分配, 并且运用遗传算法(GA)来确定相关的权重。以大学生对康师傅红烧牛肉面的评价数据为例, 运用基于遗传算法的模糊综合评价方法, 发现男生采用了“最大最小”合成算子, 女生采用了“有界和、取小”合成算子。研究结果表明, 基于遗传算法的模糊综合评价方法可以对利克特量表的心理测量数据进行有效的分析。     

Analysis of regional environment and regional innovation strategy based on cybernetics

In this paper, based on the perspective of carbon emissions, the regional environment and regional innovation strategies were analyzed by using cybernetics. The main idea was to calibrate the diesel engine simulation model with predictive function based on the data collected from the bench test, and then according to the simulation model, the genetic algorithm was applied to optimize the EGR parameters and fuel injection parameters with the target of emission generation and output torque. First of all, the combustion process and emission performance of diesel engine with internal exhaust re-circulation were studied by genetic algorithm, and the genetic algorithm model was constructed. Then, the genetic algorithm program was written based on Matlab. In addition, taking the NOx emission as the target, and the Soot emission and the output torque of the original machine as constraints, through the joint simulation of Matlab and GT-Power, the EGR parameters and fuel injection parameters of the 10% to 50% load conditions were optimized at the speed of 1500 r/min and 2000 r/min. Finally, the validity of this method was verified and analyzed.     

Computer Implication and the Curry Paradox

There are theoretical limitations to what can be implemented by a computer program. In this paper we are concerned with a limitation on the strength of computer implemented deduction. We use a version of the Curry paradox to arrive at this limitation.     

Inferring latent class lexicographic rules from choice data

A lexicographic rule orders multi-attribute alternatives in the same way as a dictionary orders words. Although no utility function can represent lexicographic preference over continuous, real-valued attributes, a constrained linear model suffices for representing such preferences over discrete attributes. We present an algorithm for inferring lexicographic structures from choice data. The primary difficulty in using such data is that it is seldom possible to obtain sufficient information to estimate individual-level preference functions. Instead, one needs to pool the data across latent clusters of individuals. We propose a method that identifies latent clusters of subjects, and estimates a lexicographic rule for each cluster. We describe an application of the method using data collected by a manufacturer of television sets. We compare the predictions of the model with those obtained from a finite-mixture, multinomial-logit model.     

Techniques for rotating two or more loading matrices to optimal agreement and simple structure: A comparison and some technical details

Matrices of factor loadings are often rotated to simple structure. When more than one loading matrix is available for the same variables, the loading matrices can be compared after rotating them all (separately) to simple structure. An alternative procedure is to rotate them to optimal agreement, and then compare them. In the present paper techniques are described that combine these two procedures. Specifically, five techniques that combine the ideals of rotation to optimal agreement and rotation to simple structure are compared on the basis of contrived and empirical data. For the contrived data, it is assessed to what extent the rotations recover the underlying common structure. For both the contrived and the empirical data it is studied to what extent the techniques give well matching rotated matrices, to what extent these have a simple structure, and to what extent the most prominent parts of the different loading matrices agree. It was found that the simple procedure of combining a Generalized Procrustes Analysis (GPA) with Varimax on the mean of the matched loading matrices performs very well on all criteria, and, for most purposes, offers an attractive compromise of rotation to agreement and simple structure. In addition to this comparison, some technical improvements are proposed for Bloxom's rotation to simple structure and maximum similarity.This research has been made possible by a fellowship from the Royal Netherlands Academy of Arts and Sciences to the author. The author is obliged to René van der Heijden for assistance in programming the procedures in the simulation study reported in this paper, and to Jos ten Berge, three anonymous reviewers and an associate editor for helpful comments on an earlier version of this paper.