Mining Images of Material Nanostructure Data

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Mining Images of Material Nanostructure Data

• Aparna S. Varde, Jianyu Liang,
• Elke A. Rundensteiner and Richard D. Sisson Jr.

ICDCIT December
2006 Bhubaneswar, India
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Introduction

• Data Mining Process of discovering interesting
  patterns in data sets
• Mining Scientific Data
• Bioinformatics
• Materials Science
• Nanotechnology

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Nanotechnology

• Field that involves
• Design, characterization, production, application
  of
• Structures, devices and systems by controlling
• Shape, size, structure and chemistry of materials
  
• At the nanoscale level
• Data from nanotechnology
• Images of nanostructures

Carbon Nanofibers
Cobalt Nanowire Arrays
Silicon Nanopore Array
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Domain-Specific Analysis

• What is the difference in nanostructure at
  various locations of a given sample?
• How does the nanostructure evolve at different
  stages of a physical / chemical / biochemical
  process?
• How does processing under different conditions
  affect interactions at the same stage of a
  process?

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Goals of Analysis in Applications

• Fabrication of biological nanostructures
• Materials for implants in human body
• Building computational tools
• Useful for tutoring, simulation, estimation
• Selection of materials for industrial processes
• Studying smaller samples helps large scale
  selection

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Image Mining Techniques

• Clustering
• Similarity Search

Target Image
Top 4 Matches
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Challenges in Mining Nanostructure Image Data

• Learning Notion of Similarity
• Defining Interestingness Measures
• Visualizing Mining Results

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Learning Notion of Similarity

• Some features of images may be more important
  than others
• Experts at best have subjective notions of
  similarity
• Need to learn a similarity measure that captures
  domain semantics

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Domain Semantics

• Nanoparticle size
• Dimension of each particle in nanostructure
• Inter-particle distance
• Distance between particles in 2-D space
• Nanoparticle height
• Projection of particles above surface
• Zoom
• Level of magnification of images
• Location
• Part of sample where image taken

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Proposed Learning Approach FeaturesRank

• Given Training samples with pairs of images and
  levels of similarity identified
• Learn Distance function that incorporates image
  features and their relative importance
• Process Iterative approach
• Use guessed initial distance function
• Compare obtained clusters with training samples
• Adjust function based on error between clusters
  and samples
• Return distance function with minimal error

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Issues in FeaturesRank

• Defining suitable notion of error
• Proposing weight adjustment heuristics
• Assessing effectiveness of learned distance
  function
• Addressed in our paper VRJSL07

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Defining Interesting Measures

• What is interesting to the user
• Assessment of mining results
• Displaying the answers
• Objective measures for interestingness
• Take into account targeted applications
• Our work on cluster representatives VRRMS06
• Minimum Description Length principle

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Visualizing Mining Results

• Potential use of Visualization Techniques for
  Multidimensional Data
• Example Star glyphs plot for heat transfer
  curves VTRWMS03

Vertex Attribute Distance from center of star
Value
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Related Work

• Similarity Search in Multimedia Databases
  KB04 Overview metrics, do not learn a
  function
• Interestingness Measures for Association Rules,
  Decision Trees HK01 Objective measures, not
  directly applicable to our work, draw an analogy
• XMDV Tool for Visualization of Multivariate Data
  W94 Possible adaptation in this context

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Conclusions

• Mining Nanostructure Images
• Domain Specific Analysis
• Targeted Applications
• Biological Nanostructures
• Computational Tools
• Industrial Processes
• Challenges
• Learning Notion of Similarity
• Defining Interestingness Measures
• Visualizing Mining Results