WordNet: a lexical database for English
Communications of the ACM
Stemming algorithms: a case study for detailed evaluation
Journal of the American Society for Information Science - Special issue: evaluation of information retrieval systems
Websom for Textual Data Mining
Artificial Intelligence Review - Special issue on data mining on the Internet
A vector space model for automatic indexing
Communications of the ACM
FOCI: flexible organizer for competitive intelligence
Proceedings of the tenth international conference on Information and knowledge management
Managing the Knowledge contained in Electronic Documents: a Clustering Method for Text Mining
DEXA '01 Proceedings of the 12th International Workshop on Database and Expert Systems Applications
Evaluating Keyword Selection Methods for WEBSOM Text Archives
IEEE Transactions on Knowledge and Data Engineering
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Data Mining: How Research Meets Practical Development?
Knowledge and Information Systems
Mining massive document collections by the WEBSOM method
Information Sciences: an International Journal - Special issue: Soft computing data mining
Text analysis and knowledge mining system
IBM Systems Journal
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Tapping the power of text mining
Communications of the ACM - Privacy and security in highly dynamic systems
Text mining techniques for patent analysis
Information Processing and Management: an International Journal
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 2
Statistical Language Models for Information Retrieval A Critical Review
Foundations and Trends in Information Retrieval
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
WordNet-based text document clustering
ROMAND '04 Proceedings of the 3rd Workshop on RObust Methods in Analysis of Natural Language Data
The needs and benefits of Text Mining applications on Post-Project Reviews
Computers in Industry
Text document clustering based on neighbors
Data & Knowledge Engineering
Virtual metrology for run-to-run control in semiconductor manufacturing
Expert Systems with Applications: An International Journal
Semantic Annotation of Aerospace Problem Reports to Support Text Mining
IEEE Intelligent Systems
A text mining approach for definition question answering
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Expert Systems with Applications: An International Journal
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Extracting salient dimensions for automatic SOM labeling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Inspection reports, commonly called ''punches'' in the marine structuring domain, are written documents about defects or supplementations on marine structures. Analyzing the inspection reports improves the construction process for the structure and prevents additional ''punches.'' This consequently reduces construction delays and supplementary costs. The free-form texts of the reports, however, hinder management from understanding the nature of defects. Therefore, we applied Knowledge Discovery in the Textual Databases (KDT) process to answer the questions, ''what kinds of defects are reported while inspecting a marine structure, and which of them are closely related?'' In particular, we propose a concept extraction and linkage approach as an ''add-on'' module for the Self-Organizing Map (SOM), a clustering algorithm for document organization. A purely data-driven graph is derived for defect-types, which gives it in an easy-to-understand form for domain experts and reduces the gap between data analysis and its practical use. Interpretation with domain experts showed that our KDT process is useful in understanding the nature of defects in the domain and systematically responding to some other related defects.