Algorithms for clustering data
Algorithms for clustering data
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Word association norms, mutual information, and lexicography
Computational Linguistics
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Two-level document ranking using mutual information in natural language information retrieval
Information Processing and Management: an International Journal
Chinese text retrieval without using a dictionary
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting Background Information in Knowledge Discovery from Text
Journal of Intelligent Information Systems
Mining Text Using Keyword Distributions
Journal of Intelligent Information Systems
Generation and search of clustered files
ACM Transactions on Database Systems (TODS)
Modern Systems Analysis and Design
Modern Systems Analysis and Design
Information Retrieval: Algorithms and Heuristics
Information Retrieval: Algorithms and Heuristics
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
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
A change detection method for sequential patterns
Decision Support Systems
Parallel teams for knowledge creation: Role of collaboration and incentives
Decision Support Systems
Knowledge discovery of weighted RFM sequential patterns from customer sequence databases
Journal of Systems and Software
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This study integrates information retrieval and data mining techniques to discover project team coordination patterns from project documents written in Chinese. The coordination pattern of a project team describes the project execution process, including task category, execution sequence and duration, as well as the team member cooperation. The integration comprises two phases. The first phase extracts the most relevant keywords describing tasks executed by projects from unstructured or semi-structured documents using the mutual information estimate and the term weighting system. A concept hierarchy tree generated using the hierarchical clustering technique represents multiple levels of task categories. The second phase discovers project team coordination patterns through sequential pattern analysis. The proposed approach obtains encouraging results by mining coordination patterns from information system development projects. In the present era of the knowledge economy, the application of groupware to facilitate team coordination and collaboration streamlines the collection and analysis of project documents throughout the project life cycle. A project manager can visualize the project execution process of a team, and can anticipate the project outcomes based on discovered team coordination patterns. Accordingly, the proposed approach can be adapted to team projects that share certain characteristics with information system development projects.