Knowledge discovery in databases: an overview
AI Magazine
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Principles of data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Incrementally fast updated frequent pattern trees
Expert Systems with Applications: An International Journal
A visual-analytic toolkit for dynamic interaction graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
FpViz: a visualizer for frequent pattern mining
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
FIsViz: a frequent itemset visualizer
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
DFP-Growth: an efficient algorithm for mining frequent patterns in dynamic database
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Scalable technique to discover items support from trie data structure
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
EFP-M2: efficient model for mining frequent patterns in transactional database
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
| Hi-index | 0.00 |
In data mining, visual representation can help in enhancing the ability of analyzing and understanding the techniques, patterns and their integration. Recently, there are varieties of visualizers have been proposed in marketplace and knowledge discovery communities. However, the detail visualization processes for constructing any incremental tree data structure from its original dataset are rarely presented. Essentially, graphic illustrations of the complex processes are easier to be understood as compared to the complex computer pseudocode. Therefore, this paper explains the visualization process of constructing our incremental Disorder Support Trie Itemset (DOSTrieIT) data structure from the flat-file dataset. DOSTrieIT can be used later as a compressed source of information for building Frequent Pattern Tree (FP-Tree). To ensure understandability, an appropriate dataset and its processes are graphically presented and details explained.