Clumping properties of content-bearing words
Journal of the American Society for Information Science
Data mining solutions: methods and tools for solving real-world problems
Data mining solutions: methods and tools for solving real-world problems
TOPIC ISLANDS—a wavelet-based text visualization system
Proceedings of the conference on Visualization '98
Hierarchical parallel coordinates for exploration of large datasets
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Multi-Faceted Insight Through Interoperable Visual Information Analysis Paradigms
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Visualizing Decision Table Classifiers
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Visualizing the non-visual: spatial analysis and interaction with information from text documents
INFOVIS '95 Proceedings of the 1995 IEEE Symposium on Information Visualization
Research Report: Volume Rendering for Relational Data
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
Visualizing Association Rules for Text Mining
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Guiding knowledge discovery through interactive data mining
Managing data mining technologies in organizations
Bursty and Hierarchical Structure in Streams
Data Mining and Knowledge Discovery
Visual data mining in software archives
SoftVis '05 Proceedings of the 2005 ACM symposium on Software visualization
Parameter free bursty events detection in text streams
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Sequential patterns for text categorization
Intelligent Data Analysis
Sequential Document Visualization
IEEE Transactions on Visualization and Computer Graphics
A Term Distribution Visualization Approach to Digital Forensic String Search
VizSec '08 Proceedings of the 5th international workshop on Visualization for Computer Security
Term distribution visualizations with Focus+Context
Proceedings of the 2009 ACM symposium on Applied Computing
Mining frequent episodes for relating financial events and stock trends
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Sequential patterns mining with fuzzy time-intervals
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
ACM SIGKDD Explorations Newsletter
Term distribution visualizations with Focus+Context
Multimedia Tools and Applications
Event-based concepts for user-driven visualization
Information Visualization
RadialViz: an orientation-free frequent pattern visualizer
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Story graphs: Tracking document set evolution using dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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A sequential pattern in data mining is a finite series of elements such as A .B .C .D where A, B, C, and D are elements of the same domain. The mining of sequential patterns is designed to find patterns of discrete events that frequently happen in the same arrangement along a timeline. Like association and clustering, the mining of sequential patterns is among the most popular knowledge discovery techniques that apply statistical measures to extract useful information from large datasets. As our computers become more powerful, we are able to mine bigger datasets and obtain hundreds of thousands of sequential patterns in full detail. With this vast amount of data, we argue that neither data mining nor visualization by itself can manage the information and reflect the knowledge effectively. Subsequently, we apply visualization to augment data mining in a study of sequential patterns in large text corpora. The result shows that we can learn increasingly quickly in an integrated visual data-mining environment.