Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data

Binning and truncation of data are common in data analysis and machine learning. This paper addresses the problem of fitting mixture densities to multivariate binned and truncated data. The EM approach proposed by McLachlan and Jones (Biometrics, 44: 2, 571-578, 1988) for the univariate case is generalized to multivariate measurements. The multivariate solution requires the evaluation of multidimensional integrals over each bin at each iteration of the EM procedure. Naive implementation of the procedure can lead to computationally inefficient results. To reduce the computational cost a number of straightforward numerical techniques are proposed. Results on simulated data indicate that the proposed methods can achieve significant computational gains with no loss in the accuracy of the final parameter estimates. Furthermore, experimental results suggest that with a sufficient number of bins and data points it is possible to estimate the true underlying density almost as well as if the data were not binned. The paper concludes with a brief description of an application of this approach to diagnosis of iron deficiency anemia, in the context of binned and truncated bivariate measurements of volume and hemoglobin concentration from an individual's red blood cells.

Geospatial clustering in data-rich environments: Features and issues

Geospatial clustering must be designed in such a way that it takes into account the special features of geoinformation and the peculiar nature of geographical environments in order to successfully derive geospatially interesting global concentrations and localized excesses. This paper examines families of geospaital clustering recently proposed in the data mining community and identifies several features and issues especially important to geospatial clustering in data-rich environments.

On dendrites in Down syndrome and DS murine models: a spiny way to learn

Since the discovery in the 1970s that dendritic abnormalities in cortical pyramidal neurons are the most consistent pathologic correlate of mental retardation, research has focused on how dendritic alterations are related to reduced intellectual ability. Due in part to obvious ethical problems and in part to the lack of fruitful methods to study neuronal circuitry in the human cortex, there is little data about the microanatomical contribution to mental retardation. The recent identification of the genetic bases of some mental retardation associated alterations, coupled with the technology to create transgenic animal models and the introduction of powerful sophisticated tools in the field of microanatomy, has led to a growth in the studies of the alterations of pyramidal cell morphology in these disorders. Studies of individuals with Down syndrome, the most frequent genetic disorder leading to mental retardation, allow the analysis of the relationships between cognition, genotype and brain microanatomy. In Down syndrome the crucial question is to define the mechanisms by which an excess of normal gene products, in interaction with the environment, directs and constrains neural maturation, and how this abnormal development translates into cognition and behaviour. In the present article we discuss mainly Down syndrome-associated dendritic abnormalities and plasticity and the role of animal models in these studies. We believe that through the further development of such approaches, the study of the microanatomical substrates of mental retardation will contribute significantly to our understanding of the mechanisms underlying human brain disorders associated with mental retardation. (C) 2004 Elsevier Ltd. All rights reserved.

Rumors of the death of emotional intelligence in organizational behavior are vastly exaggerated

In the first of two articles presenting the case for emotional intelligence in a point/counterpoint exchange, we present a brief summary of research in the field, and rebut arguments against the construct presented in this issue.We identify three streams of research: (1) a four-branch abilities test based on the model of emotional intelligence defined in Mayer and Salovey (1997); (2) self-report instruments based on the Mayer–Salovey model; and (3) commercially available tests that go beyond the Mayer–Salovey definition. In response to the criticisms of the construct, we argue that the protagonists have not distinguished adequately between the streams, and have inappropriately characterized emotional intelligence as a variant of social intelligence. More significantly, two of the critical authors assert incorrectly that emotional intelligence research is driven by a utopian political agenda, rather than scientific interest. We argue, on the contrary, that emotional intelligence research is grounded in recent scientific advances in the study of emotion; specifically regarding the role emotion plays in organizational behavior. We conclude that emotional intelligence is attracting deserved continuing research interest as an individual difference variable in organizational behavior related to the way members perceive, understand, and manage their emotions.

The case for the ability-based model of emotional intelligence in organizational behavior

In this second counterpoint article, we refute the claims of Landy, Locke, and Conte, and make the more specific case for our perspective, which is that ability-based models of emotional intelligence have value to add in the domain of organizational psychology. In this article, we address remaining issues, such as general concerns about the tenor and tone of the debates on this topic, a tendency for detractors to collapse across emotional intelligence models when reviewing the evidence and making judgments, and subsequent penchant to thereby discount all models, including the ability-based one, as lacking validity. We specifically refute the following three claims from our critics with the most recent empirically based evidence: (1) emotional intelligence is dominated by opportunistic academics-turned-consultants who have amassed much fame and fortune based on a concept that is shabby science at best; (2) the measurement of emotional intelligence is grounded in unstable, psychometrically flawed instruments, which have not demonstrated appropriate discriminant and predictive validity to warrant/justify their use; and (3) there is weak empirical evidence that emotional intelligence is related to anything of importance in organizations. We thus end with an overview of the empirical evidence supporting the role of emotional intelligence in organizational and social behavior.

Emotional awareness and emotional intelligence in leadership teaching

Recent research has highlighted the importance of emotional awareness and emotional intelligence in organizations, and these topics are attracting increasing attention. In this article, the authors present the results of a preliminary classroom study in which emotion concepts were incorporated into an undergraduate leadership course. In the study, students completed self report and ability tests of emotional intelligence. The test results were compared with students' interest in emotions and their performance in the course assessment. Results showed that interest in and knowledge of emotional intelligence predicted team performance, whereas individual performance was related to emotional intelligence.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Integrating attribute and space characteristics in choropleth display and spatial data mining

This paper develops an interactive approach for exploratory spatial data analysis. Measures of attribute similarity and spatial proximity are combined in a clustering model to support the identification of patterns in spatial information. Relationships between the developed clustering approach, spatial data mining and choropleth display are discussed. Analysis of property crime rates in Brisbane, Australia is presented. A surprising finding in this research is that there are substantial inconsistencies in standard choropleth display options found in two widely used commercial geographical information systems, both in terms of definition and performance. The comparative results demonstrate the usefulness and appeal of the developed approach in a geographical information system environment for exploratory spatial data analysis.

Stem cells in the aetiopathogenesis and therapy of rheumatic disease

Animal models of autoimmune disease and case reports of patients with these diseases who have been involved in bone marrow transplants have provided important data implicating the haemopoietic stem cell in rheumatic disease pathogenesis. Animal and human examples exist for both cure and transfer of rheumatoid arthritis, systemic lupus erythematosus (SLE) and other organ-specific diseases using allogeneic haemopoietic stem cell transplantation. This would suggest that the stem cell in these diseases is abnormal and could be cured by replacement of a normal stem cell although more in vitro data are required in this area. Given the morbidity and increased mortality in some patients with severe autoimmune diseases and the increasing safety of autologous haemopoietic stem cell transplantation (HSCT), pilot studies have been conducted using HSCT in rheumatic diseases. It is still unclear whether an autologous graft will cure these diseases but significant remissions have been obtained which have provided important data for the design of randomized trials of HSCT versus more conventional therapy. Several trials are now open to accrual under the auspices of the European Bone Marrow Transplant Group/European League Against Rheumatism (EBMT/EULAR) registry. Future clinical and laboratory research will need to document the abnormalities of the stem cell of a rheumatic patient because new therapies based on gene therapy or stem cell differentiation could be apllied to these diseases. With increasing safety of allogeneic HSCT it is not unreasonable to predict cure of some rheumatic diseases in the near future.

Long-term trends in sunshine duration and its association with schizophrenia birth rates and age at first registration - data from Australia and the Netherlands

Back ground. Based on the well-described excess of schizophrenia births in winter and spring, we hypothesised that individuals with schizophrenia (a) would be more likely to be born during periods of decreased perinatal sunshine, and (b) those born during periods of less sunshine would have an earlier age of first registration. Methods. We undertook an ecological analysis of long-term trends in perinatal sunshine duration and schizophrenia birth rates based on two mental health registers (Queensland. Australia n = 6630; The Netherlands n = 24, 474). For each of the 480 months between 1931 and 1970, the agreement between slopes of the trends in psychosis and long-term sunshine duration series were assessed. Age at first registration was assessed by quartiles of long-term trends in perinatal sunshine duration, Males and females were assessed separately. Results. Both the Dutch and Australian data showed a statistically significant association between falling long-term trends in sunshine duration around the time of birth and rising schizophrenia birth rates for males only. In both the Dutch and Australian data there were significant associations between earlier age of first registration and reduced long-term trends in sunshine duration around the time of birth for both males and females, Conclusions. A measure of long-term trends in perinatal sunshine duration was associated with two epidemiological features of schizophrenia in two separate data sets. Exposures related to sunshine duration warrant further consideration in schizophrenia research. (C) 2002 Elsevier Science B.V. All rights reserved.

Stress correlation function evolution in lattice solid elasto-dynamic models of shear and fracture zones and earthquake prediction

It has been argued that power-law time-to-failure fits for cumulative Benioff strain and an evolution in size-frequency statistics in the lead-up to large earthquakes are evidence that the crust behaves as a Critical Point (CP) system. If so, intermediate-term earthquake prediction is possible. However, this hypothesis has not been proven. If the crust does behave as a CP system, stress correlation lengths should grow in the lead-up to large events through the action of small to moderate ruptures and drop sharply once a large event occurs. However this evolution in stress correlation lengths cannot be observed directly. Here we show, using the lattice solid model to describe discontinuous elasto-dynamic systems subjected to shear and compression, that it is for possible correlation lengths to exhibit CP-type evolution. In the case of a granular system subjected to shear, this evolution occurs in the lead-up to the largest event and is accompanied by an increasing rate of moderate-sized events and power-law acceleration of Benioff strain release. In the case of an intact sample system subjected to compression, the evolution occurs only after a mature fracture system has developed. The results support the existence of a physical mechanism for intermediate-term earthquake forecasting and suggest this mechanism is fault-system dependent. This offers an explanation of why accelerating Benioff strain release is not observed prior to all large earthquakes. The results prove the existence of an underlying evolution in discontinuous elasto-dynamic, systems which is capable of providing a basis for forecasting catastrophic failure and earthquakes.

On the dimensionality of the Attachment Style Questionnaire in Italian clinical and nonclinical participants

This study examined the utility of the Attachment Style Questionnaire (ASQ) in an Italian sample of 487 consecutively admitted psychiatric participants and an independent sample of 605 nonclinical participants. Minimum average partial analysis of data from the psychiatric sample supported the hypothesized five-factor structure of the items; furthermore, multiple-group component analysis showed that this five-factor structure was not an artifact of differences in item distributions. The five-factor structure of the ASQ was largely replicated in the nonclinical sample. Furthermore, in both psychiatric and nonclinical samples, a two-factor higher order structure of the ASQ scales was observed. The higher order factors of Avoidance and Anxious Attachment showed meaningful relations with scales assessing parental bonding, but were not redundant with these scales. Multivariate normal mixture analysis supported the hypothesis that adult attachment patterns, as measured by the ASQ, are best considered as dimensional constructs.