1 Mining Images of Material Nanostructure Data Aparna S. Varde, Jianyu Liang, Elke A. Rundensteiner and Richard D. Sisson Jr. ICDCIT December 2006 Bhubaneswar, India

2 Introduction Data Mining: Process of discovering interesting patterns in data sets Mining Scientific Data Bioinformatics Materials Science Nanotechnology

3 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

4 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?

5 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

6 Image Mining Techniques Clustering Similarity Search Target ImageTop 4 Matches

7 Challenges in Mining Nanostructure Image Data Learning Notion of Similarity Defining Interestingness Measures Visualizing Mining Results

8 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

9 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

10 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

11 Issues in FeaturesRank Defining suitable notion of error Proposing weight adjustment heuristics Assessing effectiveness of learned distance function Addressed in our paper [VRJSL:07]

12 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 [VRRMS:06] Minimum Description Length principle

13 Visualizing Mining Results Potential use of Visualization Techniques for Multidimensional Data Example: Star glyphs plot for heat transfer curves [VTRWMS:03] Vertex: Attribute Distance from center of star: Value

14 Related Work Similarity Search in Multimedia Databases [KB:04]: Overview metrics, do not learn a function Interestingness Measures for Association Rules, Decision Trees [HK:01]: Objective measures, not directly applicable to our work, draw an analogy XMDV Tool for Visualization of Multivariate Data [W:94]: Possible adaptation in this context

15 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