C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
RuleViz: a model for visualizing knowledge discovery process
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Self-Organizing Maps
Auto Visual: Rule-Based Design of Interactive Multivariate Visualizations
IEEE Computer Graphics and Applications
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Assisted visualization of e-commerce auction agents
GRIN'01 No description on Graphics interface 2001
A visualization model of interactive knowledge discovery systems and its implementations
Information Visualization
Interactive data summarization: an example application
Proceedings of the working conference on Advanced visual interfaces
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Value and Relation Display for Interactive Exploration of High Dimensional Datasets
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
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Visualization has become integral to the knowledge discovery process across various domains. However, challenges remain in the effective use of visualization techniques, especially when displaying, exploring and analyzing large, multidimensional datasets, such as weather and meteorological data. Direct visualizations of such datasets tend to produce images that are cluttered with excess detail and are ineffective at communicating information at higher levels of abstraction. To address this problem we provide a visual summarization framework to intuitively reduce the data to its important and relevant characteristics. Summarization is performed in three broad steps. First, high-relevance data elements and clusters of similar data attributes are identified to reduce a dataset's size and dimensionality. Next, patterns, relationships and outliers are extracted from the reduced data. Finally, the extracted summary characteristics are visualized to the user. Such visualizations reduce excess visual detail and are more suited to the rapid comprehension of complex data. Users can interactively guide the summarization process gaining insight into both how and why the summary results are produced. Our framework improves the benefits of mathematical analysis and interactive visualization by combining the strengths of the computer and the user to generate high-quality summaries. Initial results from applying our framework to large weather datasets have been positive, suggesting that our approach could be beneficial for a wide range of domains and applications.