A visual method for high-dimensional data cluster exploration

Visualization is helpful for clustering high dimensional data. The goals of visualization in data mining are exploration, confirmation and presentation of the clustering results. However, the most of visual techniques developed for cluster analysis are primarily focused on cluster presentation rather than cluster exploration. Several techniques have been proposed to explore cluster information by visualization, but most of them depend heavily on the individual user's experience. Inevitably, this incurs subjectivity and randomness in the clustering process. In this paper, we employ the statistical features of datasets as predictions to estimate the number of clusters by a visual technique called HOV3. This approach mitigates the problem of the randomness and subjectivity of the user during the process of cluster exploration by other visual techniques. As a result, our approach provides an effective visual method for cluster exploration. © 2009 Springer-Verlag Berlin Heidelberg

Document Collection Visualization and Clustering Using An Atom Metaphor for Display and Interaction

Visual Data Mining have proven to be of high value in exploratory data analysis and data mining because it provides an intuitive feedback on data analysis and support decision-making activities. Several visualization techniques have been developed for cluster discovery such as Grand Tour, HD-Eye, Star Coordinates, etc. They are very useful tool which are visualized in 2D or 3D; however, they have not simple for users who are not trained. This thesis proposes a new approach to build a 3D clustering visualization system for document clustering by using k-mean algorithm. A cluster will be represented by a neutron (centroid) and electrons (documents) which will keep a distance with neutron by force. Our approach employs quantified domain knowledge and explorative observation as prediction to map high dimensional data onto 3D space for revealing the relationship among documents. User can perform an intuitive visual assessment of the consistency of the cluster structure

Impacts of User Heterogeneity and Attitudinal Factors on Roadway Pricing Analysis - Investigation of Value of Time and Value of Reliability for Managed Lane Facilities in South Florida

Managed lane refers to the application of various operational and design strategies on highway facilities to improve system efficiency and mobility by proactively allocating traffic capacity to different lanes. One of the key elements to understand the behavior changes and underlying causalities in user responses to managed lanes is to examine the value of time (VOT) and value of reliability (VOR). The breadth of this dissertation encompasses two major dimensions of VOT and VOR estimation – distributions or variations across different users and under different circumstances; and influences of unobserved attitudinal characteristics on roadway pricing valuation. To understand travelers’ choice behavior regarding the usage of managed lanes, combined revealed preference (RP) and stated preference (SP) data were used in this study. Mixed logit modeling was applied as the state of the art methodology to capture heterogeneity in users’ choice behavior. The model revealed an average value of $10.68 per hour for VOT and$13.91 per hour for VOR, which are reasonable considering the average household income in the region, and are well within the ranges found in the literature. In terms of user heterogeneity, the mixed logit model was further enhanced by adding interaction effects of variables, which helped recognize and quantify potential sources of heterogeneity in user sensitivities to time, reliability, and cost. The findings indicated that travelers were likely to exhibit higher willingness to pay when they were female, younger (years), older (\u3e54 years), had higher income (\u3e 50 K), driving alone, and traveled on weekdays. Attitudinal aspects are rarely incorporated into roadway pricing analysis. The study herein presents an effort to explore the role of attitudinal factors in drivers’ propensity toward using managed lanes. Model results boded for a significant contribution of attitudinal parameters in the model, both in terms of coefficients and model performance. This study provides a robust approach to quantify user heterogeneity in VOT and VOR and capture the impacts of attitudinal attributes in pricing valuation. The results of this study contribute to a better understanding on what attributes lead to higher or lower VOT and VOR and to what extent

Consumer response to road pricing: Operational and demographic effects

The High Occupancy Vehicle (HOV) lanes on Atlanta, Georgia’s radial I-85 had long been providing sub-optimal throughput in the peak traffic hours, as the two-person occupancy requirement allowed the lanes to become heavily congested. The Georgia Department of Transportation converted 15.5 miles of HOV 2+ lanes to High Occupancy Toll (HOT) lanes, one in each direction on I-85. The lanes use dynamic value pricing to set toll levels based on the volume and average speed of traffic in the lanes. The goal of this research was to investigate the responses to toll lane pricing and the factors that appear to inform lane choice decisions, as well as examining values of travel time savings and toll price elasticity for users of the Express Lanes. This study of the metropolitan Atlanta I-85 Express Lanes operates at the microscopic level to examine the impact of demographic characteristics, congestion levels, and pricing on users’ decisions to use or not use the I-85 Express Lanes. The dissertation examined the value of travel time savings distributions across income segments. The differences in these distributions among lower, medium, and higher income households were marginal at best. The results did not indicate that higher income households had the highest value of travel time savings results, as may have been expected. The modeling work performed here provided a number of insights into toll lane use. The determinants of lane choice decision-making in the morning peak had notable differences from the determinants of the afternoon peak. The initial analysis involved models which were estimated across three different income segments to examine differences in decision making between low, medium, and higher income households. The results indicated that the parameters were largely consistent across the three segments. Further segmenting the households showed that lane choice determinants varied more within the ‘Higher’ income segment than across the original three-segment structure. In particular, the five-segment models illustrated lower elasticities with regard to corridor segment counts and toll levels for the highest-income households in the sample, as well as higher household income level elasticities for afternoon trips by that same cohort. The research was among the first in the available literature to use revealed preference lane use data for both the toll lane users and the unpriced general purpose lane users. The use of household level marketing data, rather than census or survey data, was another unique characteristic of this research. The analysis of value of travel time savings with a demographic component that looks at household income has not yet been seen in the literature; similarly, the findings regarding differing behavior among very high income households appear to be unseen in the existing literature. The results from this analysis, such as willingness-to-pay values for different population segments, will be useful inputs to the decisions surrounding future HOT implementations in the Atlanta region. The use of new data sources, the evaluation of those types of data sources, and the application of methods that have previously been unused in this field make up the primary contributions of this dissertation.Ph.D

Careers, commitment and work involvement : a study of work and career attitudes amongst professional engineers

Imperial Users onl