Spotfire: an information exploration environment
ACM SIGMOD Record
Visual classification: an interactive approach to decision tree construction
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing association rules with interactive mosaic plots
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
RuleViz: a model for visualizing knowledge discovery process
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Query, analysis, and visualization of hierarchically structured data using Polaris
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
OSSM: A Segmentation Approach to Optimize Frequency Counting
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
A two-way visualization method for clustered data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient dynamic mining of constrained frequent sets
ACM Transactions on Database Systems (TODS)
IEEE Computer Graphics and Applications
Pruning and Visualizing Generalized Association Rules in Parallel Coordinates
IEEE Transactions on Knowledge and Data Engineering
AC-Close: Efficiently Mining Approximate Closed Itemsets by Core Pattern Recovery
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
From frequent itemsets to semantically meaningful visual patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive visual exploration of association rules with rule-focusing methodology
Knowledge and Information Systems
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Probabilistic latent semantic visualization: topic model for visualizing documents
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The persuasive phase of visualization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A visual-analytic toolkit for dynamic interaction graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Maximum Entropy Based Significance of Itemsets
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
WiFIsViz: Effective Visualization of Frequent Itemsets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Finding Good Itemsets by Packing Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Session Viewer: Visual Exploratory Analysis of Web Session Logs
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
Cartesian contour: a concise representation for a collection of frequent sets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
CP-summary: a concise representation for browsing frequent itemsets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards efficient mining of proportional fault-tolerant frequent itemsets
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Mining interesting sets and rules in relational databases
Proceedings of the 2010 ACM Symposium on Applied Computing
FIsViz: a frequent itemset visualizer
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Introduction to the special issue on visual analytics and knowledge discovery
ACM SIGKDD Explorations Newsletter
FpVAT: a visual analytic tool for supporting frequent pattern mining
ACM SIGKDD Explorations Newsletter
A vector field visualization technique for self-organizing maps
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Visual analytics of social networks: mining and visualizing co-authorship networks
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
| Hi-index | 0.00 |
Numerous algorithms have been proposed since the introduction of the research problem of frequent pattern mining. Such a research problem has played an essential role in many knowledge discovery and data mining (KDD) tasks. Most of the proposed frequent pattern mining algorithms return the mined results in the form of textual lists that contain frequent patterns showing those frequently occurring sets of items. As "a picture is worth a thousand words", the use of visual representation can enhance the user understanding of the inherent relations in a collection of frequent patterns. Although a few visualizers have been developed to visualize the raw data or the results for some data mining tasks, most of these visualizers were not designed for visualizing frequent patterns. For those that were, they show all the frequent patterns that can be mined from datasets. It is not uncommon that, for many real-life applications, the user may end up be overwhelmed by such a huge number of patterns. In this paper, we propose a visualizer---called CloseViz---to show the user only the useful patterns. Specifically, CloseViz shows only closed frequent patterns. By doing so, CloseViz reduces the number of displayed patterns to a useful amount while retaining all the important frequency information. Moreover, CloseViz presents the closed frequent patterns to the user in a useful manner, which allows visual exploration of the patterns. Note that the closed patterns shown by CloseViz can be considered as surrogates for all the frequent patterns that can be mined from the datasets.