Title page for ETD etd-081506-152633

Document Type dissertation

Author Name Varde, Aparna S

URN etd-081506-152633

Title Graphical Data Mining for Computational Estimation in Materials Science Applications

Degree PhD

Department Computer Science

Advisors
Elke A. Rundensteiner, Advisor
David C. Brown, Committee Member
Carolina Ruiz, Committee Member
Neil T. Heffernan, Committee Member
Richard D. Sisson Jr., Committee Member

Keywords
Classification
Data Mining
Domain Semantics
Graphical Data
Clustering

Date of Presentation/Defense 2006-08-15

Availability unrestricted

Abstract
In domains such as Materials Science experimental results are often plotted as
two-dimensional graphs of a dependent versus an independent variable to aid visual analysis. Performing laboratory experiments with specified input conditions and plotting such graphs consumes significant time and resources motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input
conditions, and to estimate the conditions needed to obtain a desired graph. State-of-the-art estimation approaches are not found suitable for targeted applications.

In this dissertation, an estimation approach called AutoDomainMine is proposed. In AutoDomainMine, graphs from existing experiments are clustered and decision tree classification is used to learn the conditions characterizing these clusters in order to build a representative pair of input conditions and graph per cluster. This forms knowledge discovered from existing experiments. Given the conditions of a new experiment, the relevant decision tree path is traced to estimate its cluster. The representative graph of that cluster is the estimated graph. Alternatively, given a desired graph, the closest matching representative graph is found. The conditions of the corresponding representative pair are the estimated conditions.

One sub-problem of this dissertation is preserving semantics of graphs during clustering. This is addressed through our proposed technique, LearnMet, for learning domain-specific distance metrics for graphs by iteratively comparing actual and predicted clusters over a training set using a guessed initial metric in any fixed clustering algorithm and refining it until error between actual and predicted clusters is minimal or below a given threshold. Another sub-problem is capturing the relevant details of each cluster through its representative yet conveying concise
information. This is addressed by our proposed methodology, DesRept, for designing semantics-preserving cluster representatives by capturing various levels of detail in the cluster taking into account ease of interpretation and information loss based on the interests of targeted users.

The tool developed using AutoDomainMine is rigorously evaluated with real data in the Heat Treating domain that motivated this dissertation. Formal user surveys comparing the estimation with the laboratory experiments indicate that AutoDomainMine provides satisfactory estimation.

Files
avarde.pdf

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Document Type	dissertation
Author Name	Varde, Aparna S
URN	etd-081506-152633
Title	Graphical Data Mining for Computational Estimation in Materials Science Applications
Degree	PhD
Department	Computer Science
Advisors	Elke A. Rundensteiner, Advisor David C. Brown, Committee Member Carolina Ruiz, Committee Member Neil T. Heffernan, Committee Member Richard D. Sisson Jr., Committee Member
Keywords	Classification Data Mining Domain Semantics Graphical Data Clustering
Date of Presentation/Defense	2006-08-15
Availability	unrestricted
Abstract In domains such as Materials Science experimental results are often plotted as two-dimensional graphs of a dependent versus an independent variable to aid visual analysis. Performing laboratory experiments with specified input conditions and plotting such graphs consumes significant time and resources motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions needed to obtain a desired graph. State-of-the-art estimation approaches are not found suitable for targeted applications. In this dissertation, an estimation approach called AutoDomainMine is proposed. In AutoDomainMine, graphs from existing experiments are clustered and decision tree classification is used to learn the conditions characterizing these clusters in order to build a representative pair of input conditions and graph per cluster. This forms knowledge discovered from existing experiments. Given the conditions of a new experiment, the relevant decision tree path is traced to estimate its cluster. The representative graph of that cluster is the estimated graph. Alternatively, given a desired graph, the closest matching representative graph is found. The conditions of the corresponding representative pair are the estimated conditions. One sub-problem of this dissertation is preserving semantics of graphs during clustering. This is addressed through our proposed technique, LearnMet, for learning domain-specific distance metrics for graphs by iteratively comparing actual and predicted clusters over a training set using a guessed initial metric in any fixed clustering algorithm and refining it until error between actual and predicted clusters is minimal or below a given threshold. Another sub-problem is capturing the relevant details of each cluster through its representative yet conveying concise information. This is addressed by our proposed methodology, DesRept, for designing semantics-preserving cluster representatives by capturing various levels of detail in the cluster taking into account ease of interpretation and information loss based on the interests of targeted users. The tool developed using AutoDomainMine is rigorously evaluated with real data in the Heat Treating domain that motivated this dissertation. Formal user surveys comparing the estimation with the laboratory experiments indicate that AutoDomainMine provides satisfactory estimation.
Files	avarde.pdf