Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
Comparisons of Classification Methods for Screening Potential Compounds
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
The complexity of mining maximal frequent itemsets and maximal frequent patterns
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Facing Imbalanced Classes through Aggregation of Classifiers
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Survey of clustering algorithms
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In molecular fragments mining, scientists use both manual techniques and pure computer based methods. In this paper, we propose a novel molecular fragment mining approach that incorporates interactive user assistance to speed up and increase the success rates in traditional fragment mining processes. The proposed approach visualizes 3D molecular data in 2D form that can be easily interpreted by a human expert who evaluates and filters the 2D molecular images manually. The proposed approach differs from others in literature as it does not search substructures including specific atoms like graph mining methods do. Instead, user assisted approach highlights significant substructures with specific properties and topologies graphically. Initial experiments indicate that by the use of user assisted approach, active and inactive fragments of compounds are quickly determined for drug design with high success rates.