We discuss the nature of cognitive representations and present a scheme for the encoding of information which accounts for both categorical and graded aspects of cognitive events. Accordingly, object categories are mapped onto the identity of cell populations via the process of categorical perception, while graded aspects such as vividness and con dence level are mapped onto the rate and degree...

Clustering is the process of grouping a set of physical objects into classes of similar object. Objects in real world consist of both numerical and categorical data. Categorical data are not analyzed as numerical data because of the absence of inherit ordering. This paper describes about occurrence based categorical data clustering (OBCDC) technique based on cosine similarity measure and simple...

Questions: How well do GIS-derived categorical variables (e.g., vegetation, soils, geology, elevation, geography, and physiography) separate plots based on community composition? How does the ability to distinguish plots by community composition vary with spatial scale, specifically number of patch types, patch size and spatial correlation? Both these questions bear on the effective use of stra...

INTRODUCTION The Community Health Status Indicators Project was undertaken to produce county-specific reports assessing the status of community health for local jurisdictions throughout the United States. To accomplish this assessment, the Community Health Status Indicators Project team selected peer groupings of counties to monitor and analyze the health of local communities relative to peer c...

Using Multiple Identity Tracking task and the functional magnetic resonance imaging (fMRI) technology, the present study aimed to isolate and visualize the functional anatomy of neural systems involved in the semantic category-based grouping process. Three experiment conditions were selected and compared: the category-based targets grouping (TG) condition, the targets-distractors grouping (TDG)...

The method of principal components is widely used to estimate common factors in large panels of continuous data. This paper first reviews alternative methods that obtain the common factors by solving a Procrustes problem. While these matrix decomposition methods do not specify the probabilistic structure of the data and hence do not permit statistical evaluations of the estimates, they can be e...

We consider the situation of two ordered categorical variables and a binary outcome variable, where one or both of the categorical variables may have missing values. The goal is to estimate the probability of response of the outcome variable for each cell of the contingency table of categorical variables while incorporating the fact that the categorical variables are ordered. The probability of...

In this article, the performance of data mining and statistical techniques was empirically compared while varying the number of independent variables, the types of independent variables, the number of classes of the independent variables, and the sample size. Our study employed 60 simulated examples, with artificial neural networks and decision trees as the data mining techniques, and linear re...

Canonical correspondence analysis and redundancy analysis are two methods of constrained ordination regularly used in the analysis of ecological data when ordinations based on several response variables (for example, species abundances) are related linearly to several explanatory variables (for example, environmental variables, spatial positions of samples). In this report I demonstrate the adv...