List of Figures

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Chapter 1: Bayesian Networks as a Decision Support Tool in Credit Scoring Domain

Figure 1: Simple debit model-Uninitialized network

Figure 2: Initialized network after propagation procedure

Figure 3: The model of credit risk estimation

Figure 4: Bayesian network model of credit risk analysis

Figure 5: The detail of Bayesian network relating to "creditability of the enterpreneur" and "management quality"

Figure 6: Evaluation of economic-financial condition of the enterprise-"profitability" section

Figure 7: Evaluation of synthesized credit-risk level based on financial and economic analysis of an enterprise

Figure 8: The subnetwork that models influence of subjective factors on credit risk evaluation

Chapter 2: Frontier versus Ordinary Regression Models for Data Mining

Figure 1: Ceiling versus OLS regression

Figure 2: Models forced to pass through the origin

Chapter 3: An Evolutionary Misclassification Cost Minimization Approach for Medical Diagnosis

Figure 1: The misclassification cost matrix

Figure 2: Results for correct classification in the training sample for simulated data

Figure 3: Type I error in the training sample for simulated data

Figure 4: Results for correct classification in the holdout sample for simulated data

Figure 5: Type I error in holdout sample for simulated data

Figure 6: The misclassification cost matrix for heart disease data set

Chapter 4: Guiding Knowledge Discovery Through Interactive Data Mining

Figure 1: Knowledge based HCI (Dix '98)

Figure 2: Centroid-based planar clustering presentation

Figure 3: Centroid-based 3D clustering presentations

Figure 4: Density-based presentation (Sander et al., 1998)

Figure 5: Partition-based density presentation (Hinneburg & Keim, 1999b)

Figure 6: Grid-based clustering presentation with resolution variance (Sheikholeslami, Chatterjee & Zhang, 1998)

Figure 7: Scatterplot matrix projection

Figure 8: Snapshot of cluster-based projection tour (Li et al., 1995)

Figure 9: Volume rendering (Yang, 2000)

Figure 10: Partition-based hierarchical clustering

Figure 11: Hierarchical clustering planar presentation, comprised of a hierarchically partitioned space and associated dendogram (Fox, 2001)

Figure 12: H-Blob-Hierarchical clustering presentation (Sprenger et al., 2000)

Figure 13: DENCLUE-Density presentation of same data set with different thresholds indicating possible hierarchical extension (Hinneburg et al., 1999)

Figure 14: OPTICS-Reachability plot (Ankherst et al., 1999)

Figure 15: Clustering of spatially extended objects

Figure 16: COD-Clustering with obstructed distance

Figure 17: Matrix-based association presentations

Figure 18: Rule vs. item association matrix (Wong et al., 1999)

Figure 19: Mosaic plots (Hofman, Siebes & Wilhelm, 2000)

Figure 20: Rule Graph (Klemettinen et al., 1994)

Figure 21: DAV process (Hao, Dayal, Hsu, Sprenger & Gross, 2000)

Figure 22: Concentric association rule visualisation

Figure 23: Geominer-Spatial association rule visualisation

Figure 24: Temporal association mining (Rainsford & Foddick, 2000)

Figure 25: Sequential mining presentation (Wong et al., 2000)

Figure 26: Interactive views of hierarchical clustering (Wills, 1998)

Figure 27: Graphical fisheye views (Sarker & Brown, 1994)

Figure 28: Removing occlusion through distortion (Sheelagh, Carpendale, Cowperthwaite & Francis, 1997)

Figure 29: Knowledge discovery process

Figure 30: Guided knowledge discovery process

Figure 31: Batch data mining run

Figure 32: Interactive data mining run

Figure 33: Repercussions of user movement of customer from route A to B (Rabejij, 2001)

Figure 34: Routing interface snapshot (Anderson, Anderson, Lesh, Marks, Perlin, Ratajczak & Ryall, 2000)

Chapter 7: The Myth of Enterprise Database Redesign

Figure 1: Theoretical lens

Figure 2: Environment

Figure 3: Environment and people

Figure 4: Environment, people, and methodology

Figure 5: Environment, people, methodology, and IT perspective

Figure 6: Transformation component model

Figure 7: Emergent theoretical model

Chapter 8: New Information Technologies and Other Pertinent Issues Impacting the Strategic Dimension of CRM for Business Excellence

Figure 1: A typology of relationships between customers and business firms (Jenkins, 1999, p. 372)

Figure 2: Frontline information systems (FIS) for relationship marketing (Iacobucci & Ostrom, 1995, p. 559)

Chapter 10: A Multidimensional Data Warehouse Development Methodology

Figure 1: Example of domain aggregation, hierarchy and sub-hierarchy

Figure 2: Graphical representation of a fact schema

Figure 3: Cube corresponding to Figure 2

Figure 4: IDEA-DWCASE tool

Figure 5: Methodology overview

Figure 6: Dimensions detected

Figure 7: Conceptual schema

Figure 8: Star schema

Chapter 11: A Telecommunications Model for Managing Complexity of Voice and Data Networks and Services

Figure 1: Model's architecture integration in a TI environment

Figure 2: Model's packet-switching solution architecture and its components

Figure 3: Model's service architecture

Figure 4: Components of the service architecture

Chapter 12: Combination Forecasts Based on Markov Chain Monte Carlo Estimation of the Mode

Figure 1: Time series plot for arrival of U.S. citizens for foreign travel (1971-1977)

Figure 2: Weibull probability plot for the arrival of U.S. citizens from foreign travel in 1978

Chapter 13: Web Mining: Creating Structure Out of Chaos

Figure 1: An illustrative example of the hub and authority model

Figure 2: An illustrative example of the hub and spoke model

Figure 3: An illustrative example of traversal patterns

Chapter 14: Connectionist and Evolutionary Models for Learning, Discovering and Forecasting Software Effort

Figure 1: A three-layer (of nodes) artificial neural network

Figure 2: The performance comparison between the actual effort and network learned effort at convergence

Figure 3: The performance comparison between the actual effort and network predicted effort

Figure 4: Best member and average population fitness vs. learning generation of the GA run

Figure 5: The Performance Comparison between the Actual Effort and the Model Predicted Effort for the Best Fitness Population Member.

Figure 6: The performance comparison between actual effort and network predicted effort

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