Neural networks for pattern recognition
Neural networks for pattern recognition
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Similarity Search in Multimedia Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
AutoDomainMine: a graphical data mining system for process optimization
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
LearnMet: learning domain-specific distance metrics for plots of scientific functions
Multimedia Tools and Applications
Comparing mathematical and heuristic approaches for scientific data analysis
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Component Selection to Optimize Distance Function Learning in Complex Scientific Data Sets
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Mining images of material nanostructure data
ICDCIT'06 Proceedings of the Third international conference on Distributed Computing and Internet Technology
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In mining graphical data the default Euclidean distance is often used as a notion of similarity. However this does not adequately capture semantics in our targeted domains, having graphical representations depicting results of scientific experiments. It is seldom known a-priori what other distance metric best preserves semantics. This motivates the need to learn such a metric. A technique called LearnMet is proposed here to learn a domain-specific distance metric for graphical representations. Input to LearnMet is a training set of correct clusters of such graphs. LearnMet iteratively compares these correct clusters with those obtained from an arbitrary but fixed clustering algorithm. In the first iteration a guessed metric is used for clustering. This metric is then refined using the error between the obtained and correct clusters until the error is below a given threshold. LearnMet is evaluated rigorously in the Heat Treating domain which motivated this research. Clusters obtained using the learned metric and clusters obtained using Euclidean distance are both compared against the correct clusters over a separate test set. Our results show that the learned metric provides better clusters.