Highlights: language- and domain-independent automatic indexing terms for abstracting
Journal of the American Society for Information Science
Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping
IEEE Transactions on Knowledge and Data Engineering
KeyGraph: Automatic Indexing by Co-occurrence Graph based on Building Construction Metaphor
ADL '98 Proceedings of the Advances in Digital Libraries Conference
Panel: Is Visualization Struggling under the Myth of Objectivity?
VIS '95 Proceedings of the 6th conference on Visualization '95
Chance Discovery
Visualizing Competitive Behaviors in Multi-User Virtual Environments
VIS '04 Proceedings of the conference on Visualization '04
On the Use of Wavelet Decomposition for String Classification
Data Mining and Knowledge Discovery
Online algorithm for the self-organizing map of symbol strings
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Designing for Social Data Analysis
IEEE Transactions on Visualization and Computer Graphics
VU-Flow: A Visualization Tool for Analyzing Navigation in Virtual Environments
IEEE Transactions on Visualization and Computer Graphics
Cellular automata and Hilditch thinning for extraction of user paths in online games
NetGames '06 Proceedings of 5th ACM SIGCOMM workshop on Network and system support for games
Database research opportunities in computer games
ACM SIGMOD Record
ICEC'05 Proceedings of the 4th international conference on Entertainment Computing
Feature-based projections for effective playtrace analysis
Proceedings of the 6th International Conference on Foundations of Digital Games
A spatiotemporal visualization approach for the analysis of gameplay data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analysis of telemetry data from a real-time strategy game: A case study
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
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We propose a visualization approach for analyzing players' action behaviors. The proposed approach consists of two visualization techniques: classical multidimensional scaling (CMDS) and Key Graph. CMDS is for discovering clusters of players who behave similarly. Key Graph is for interpreting action behaviors of players in a cluster of interest. In order to reduce the dimension of matrices used in computation of the CMDS input, we exploit a time-series reduction technique recently proposed by us. Our visualization approach is evaluated using log of an online game where three-player types according to Bartle's taxonomy are found, that is, achievers, explorers, and socializers.