I have received a number enquiries about the research I have undertaken thus far in my project. In response, I have decided to post the resources I have included to date:
1. AGHAGOLZADEH, M., SOLTANIAN-ZADEH, H. & ARAABI, B. N. (2006) Finding the Number of Clusters in a Dataset Using an Information Theoretic Hierarchial Algorithm. IEEE, 1336-1339.
2. AHONEN, H., HEINONEN, O., KLEMETTINEN, M. & VERKAMO, A. I. (1997) Applying Data Mining Techniques for Descriptive Phrase Extraction in Digital Document Collectors. Department of Computer Science. Helsinki, University of Helsinki.
3. AMARASIRI, R., CEDDIA, J. & ALAHAKOON, D. (2005) Exploratory Data Mining Lead by Text Mining Using a Novel High Dimensional Clustering Algorithm. Fourth International Conference on Machine Learning and Applications (ICMLA'05). IEEE Computer Society.
4. ATKINSON-ABUTRIDY, J., MELLISH, C. & AITKEN, S. (2004) Combining Information Extraction with Genetic Algorithms for Text Mining. IEEE Intelligent Systems, 22-30.
5. AUMANN, Y., FELDMAN, R., YEHUDA, Y. B., LANDAU, D., LIPHSTAT, O. & SCHLER, Y. (1999) Circle Graphs: New Visualization Tools for Text-Mining. J.M. Zytkow and J. Rauch (Eds): PKDD'99, 277-282.
6. CALVO, R., JOSE, J. & ADEVA, G. (2006) Mining Text with Pimiento. IEEE Internet Computing, 27- 35.
7. CHANG, H., HSU, C. & DENG, Y. (2004) Unsupervised Document Clustering Based on Keyword Clusters. International Symposium on Communications and Information Technologies 2004 (ISCIT 2004). Sapporo, Japan.
8. CHEN, J., YAN, J., ZHANG, B., YANG, Q. & CHEN, Z. (2006) Diverse Topic Phrase Extraction through Latent Semantic Analysis. Sixth International Conference on Data Mining (ICDM'06). IEEE Computer Society.
9. CODY, W., KREULEN, J., KRISHNA, V. & SPANGLER, W. (2002) The integration of business intelligence and knowledge management. IBM Systems Journal, 41, 697-713.
11. EL-BELTAGY, S. R. (2006) KP-Miner: A Simple System for Effective Keyphrase Extraction. IEEE, 1-5.
12. FAN, W., WALLACE, L. & RICH, S. (2006) Tapping the Power of Text Mining. Communications of the ACM, 49, 77-82.
13. GLECH, D. & ZHUKOV, L. (2003) SVD Subspace Projections for Term Suggestion Ranking and Clustering. Claremont, California, Harvey Mudd College, Yahoo! Research Labs.
14. GRIMES, S. (2003) Decision Support: The Word on Text Mining. Intelligent Entreprise, 6, 12-13.
15. HOFMANN, D. G. T. (2006) Non-redundant data clustering. Knowledge and Information Systems, 1-24.
16. HSU, H. C. C. (2005) Using Topic Keyword Clusters for Automatic Document Clustering. Third International Conference on Information Technology and Applications (ICITA'05). IEEE.
17. IIRITANO, S. & RUFFOLO, S. (2001) Managing the Knowledge Contained in Electronic Documents: a Clustering Method for Text Mining. IEEE, 454-458.
18. JAIN, A. K., MURTY, M. N. & FLYNN, P. J. (1999) Data Clustering: A Review. ACM Computing Serveys, 31, 264-323.
19. JENSEN, R., II, K. E. H., ERDOGMUS, D., PRINCIPE, J. C. & ELTOFT, T. (2003) Clustering using Renyi's Entropy. IEEE, 523-528.
20. LAN, M., SUNG, S., LOW, H. & TAN, C. (2001) A Comparative Study on Term Weighting Schemes for Text Categorization. Department of Computer Science. Singapore, National University of Singapore.
21. LANDAUER, T. K. & DUMAIS, S. T. (1997) A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. Pyschological Review, 104, 211-240.
22. LANDAUER, T. K., LAHAM, D., REHDER, B. & SCHREINER, M. E. (1997) How Well can Passage Meaning be Derived without Using Word Order? A Comparison of Latent Semantic Analysis and Humans.
23. MOTES-Y-GOMEZ, M., A.GELBUKH, LOPEZ-LOPEZ, A. & BAEZA-YATES, R. (2001) Text Mining with Conceptual Graphs. IEEE, 898-903.
24. ONG, S. A. J. (2000) A Data Mining Strategy for Inductive Data Clustering: A Synergy Between Self Organising Neural Networks and K-Means Clustering Techniques. IEEE.
25. OSINSKI, S. (2006) Improving Quality of Search Results Clustering with Approximate Matrix Factorisations. M. Lalmas et al. (Eds.): ECIR 2006, 167-178.
26. OSINSKI, S. & WEISS, D. (2005) A Concept-Driven Algorithm for Clustering Search Results. IEEE Intelligent Systems, 48-54.
27. QIAN, Y. & SUEN, C. Y. (2000) Clustering Combination Method. IEEE, 732-735.
28. SHARMA, R. & RAMAN, S. (2003) Phrase-based Text Representation for Managing Web Documents. International Conference on Information Technology: Computers and Communication (ITCC'03). IEEE Computer Society.
29. SHEHATA, S., KARRAY, F. & KAMEL, M. (2006) Enhancing Text Clustering using Concept-based Mining Model. Sixth International Conference on Data Mining. IEEE Computer Society.
30. STEINBACH, M., KARYPIS, G. & KUMAR, V. (2006) A Comparison of Document Clustering Techniques. IEEE, 1-2.
31. TJHI, W. & CHEN, L. (2006) Flexible Fuzzy Co-Clustering with Feature-cluster Weighting. IEEE.
32. TSUJII, J. & ANANIADOU, S. (2005) Thesaurus or Logical Ontology, Which One Do We Need for Text Mining. Language Resources and Evaluation, 39, 77-90.
34. WEISS, S. M., APTE, C., DAMERAU, F. J., JOHNSON, D. E., J.OLES, F., GOETZ, T. & HAMPP, T. (1999) Maximizing Text-Mining Performance. IEEE, 63-69.
35. WIEMER-HASTINGS, P. & ZIPITRIA, I. (2000) Rules for Syntax, Vectors for Semantics. Edinburgh, University of Edinburgh.
36. WITTEN, I. H. & FRANK, E. (2005) Data Mining: Practical Machine Learning Tools and Techniques, San Francisco, Morgan Kaufmann Publishers.
37. WONG, P., COWLEY, W., FOOTE, H., JURRUS, E. & THOMAS, J. (2000) Visualizing Sequential Patterns for Text Mining. IEEE Synposium on Information Visualisation 2000 (InfoVis'00).
38. WU, H. & GUNOPLOUS, D. (2002) Evaluating the Utiliy of Statistical Phrases and Latent Semantic Indexing for Text Classification. IEEE, 713-716.
39. YANG, H. & LEE, C. (2003) A Text Mining Approach on Automatic Generation of Web Directories and Hierarchies. IEEE/WIC International Conference on Web Intelligence (WI'03). IEEE.
40. YU, J. (2005) General C-Means Clustering Model. IEEE Computer Society, 1197-1211.
ZELIKOVITZ, S. & HIRSH, H. (2000) Using LSI for Text Classification in the Presence of Background Text. Piscataway, New Jersey, Rutgers University.
41. ZHONG, M., CHEN, Z. & LIN, Y. (2004) Using Classification and key phrase Extraction for information retrieval. IEEE, 3037-3041.
2. AHONEN, H., HEINONEN, O., KLEMETTINEN, M. & VERKAMO, A. I. (1997) Applying Data Mining Techniques for Descriptive Phrase Extraction in Digital Document Collectors. Department of Computer Science. Helsinki, University of Helsinki.
3. AMARASIRI, R., CEDDIA, J. & ALAHAKOON, D. (2005) Exploratory Data Mining Lead by Text Mining Using a Novel High Dimensional Clustering Algorithm. Fourth International Conference on Machine Learning and Applications (ICMLA'05). IEEE Computer Society.
4. ATKINSON-ABUTRIDY, J., MELLISH, C. & AITKEN, S. (2004) Combining Information Extraction with Genetic Algorithms for Text Mining. IEEE Intelligent Systems, 22-30.
5. AUMANN, Y., FELDMAN, R., YEHUDA, Y. B., LANDAU, D., LIPHSTAT, O. & SCHLER, Y. (1999) Circle Graphs: New Visualization Tools for Text-Mining. J.M. Zytkow and J. Rauch (Eds): PKDD'99, 277-282.
6. CALVO, R., JOSE, J. & ADEVA, G. (2006) Mining Text with Pimiento. IEEE Internet Computing, 27- 35.
7. CHANG, H., HSU, C. & DENG, Y. (2004) Unsupervised Document Clustering Based on Keyword Clusters. International Symposium on Communications and Information Technologies 2004 (ISCIT 2004). Sapporo, Japan.
8. CHEN, J., YAN, J., ZHANG, B., YANG, Q. & CHEN, Z. (2006) Diverse Topic Phrase Extraction through Latent Semantic Analysis. Sixth International Conference on Data Mining (ICDM'06). IEEE Computer Society.
9. CODY, W., KREULEN, J., KRISHNA, V. & SPANGLER, W. (2002) The integration of business intelligence and knowledge management. IBM Systems Journal, 41, 697-713.
11. EL-BELTAGY, S. R. (2006) KP-Miner: A Simple System for Effective Keyphrase Extraction. IEEE, 1-5.
12. FAN, W., WALLACE, L. & RICH, S. (2006) Tapping the Power of Text Mining. Communications of the ACM, 49, 77-82.
13. GLECH, D. & ZHUKOV, L. (2003) SVD Subspace Projections for Term Suggestion Ranking and Clustering. Claremont, California, Harvey Mudd College, Yahoo! Research Labs.
14. GRIMES, S. (2003) Decision Support: The Word on Text Mining. Intelligent Entreprise, 6, 12-13.
15. HOFMANN, D. G. T. (2006) Non-redundant data clustering. Knowledge and Information Systems, 1-24.
16. HSU, H. C. C. (2005) Using Topic Keyword Clusters for Automatic Document Clustering. Third International Conference on Information Technology and Applications (ICITA'05). IEEE.
17. IIRITANO, S. & RUFFOLO, S. (2001) Managing the Knowledge Contained in Electronic Documents: a Clustering Method for Text Mining. IEEE, 454-458.
18. JAIN, A. K., MURTY, M. N. & FLYNN, P. J. (1999) Data Clustering: A Review. ACM Computing Serveys, 31, 264-323.
19. JENSEN, R., II, K. E. H., ERDOGMUS, D., PRINCIPE, J. C. & ELTOFT, T. (2003) Clustering using Renyi's Entropy. IEEE, 523-528.
20. LAN, M., SUNG, S., LOW, H. & TAN, C. (2001) A Comparative Study on Term Weighting Schemes for Text Categorization. Department of Computer Science. Singapore, National University of Singapore.
21. LANDAUER, T. K. & DUMAIS, S. T. (1997) A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. Pyschological Review, 104, 211-240.
22. LANDAUER, T. K., LAHAM, D., REHDER, B. & SCHREINER, M. E. (1997) How Well can Passage Meaning be Derived without Using Word Order? A Comparison of Latent Semantic Analysis and Humans.
23. MOTES-Y-GOMEZ, M., A.GELBUKH, LOPEZ-LOPEZ, A. & BAEZA-YATES, R. (2001) Text Mining with Conceptual Graphs. IEEE, 898-903.
24. ONG, S. A. J. (2000) A Data Mining Strategy for Inductive Data Clustering: A Synergy Between Self Organising Neural Networks and K-Means Clustering Techniques. IEEE.
25. OSINSKI, S. (2006) Improving Quality of Search Results Clustering with Approximate Matrix Factorisations. M. Lalmas et al. (Eds.): ECIR 2006, 167-178.
26. OSINSKI, S. & WEISS, D. (2005) A Concept-Driven Algorithm for Clustering Search Results. IEEE Intelligent Systems, 48-54.
27. QIAN, Y. & SUEN, C. Y. (2000) Clustering Combination Method. IEEE, 732-735.
28. SHARMA, R. & RAMAN, S. (2003) Phrase-based Text Representation for Managing Web Documents. International Conference on Information Technology: Computers and Communication (ITCC'03). IEEE Computer Society.
29. SHEHATA, S., KARRAY, F. & KAMEL, M. (2006) Enhancing Text Clustering using Concept-based Mining Model. Sixth International Conference on Data Mining. IEEE Computer Society.
30. STEINBACH, M., KARYPIS, G. & KUMAR, V. (2006) A Comparison of Document Clustering Techniques. IEEE, 1-2.
31. TJHI, W. & CHEN, L. (2006) Flexible Fuzzy Co-Clustering with Feature-cluster Weighting. IEEE.
32. TSUJII, J. & ANANIADOU, S. (2005) Thesaurus or Logical Ontology, Which One Do We Need for Text Mining. Language Resources and Evaluation, 39, 77-90.
34. WEISS, S. M., APTE, C., DAMERAU, F. J., JOHNSON, D. E., J.OLES, F., GOETZ, T. & HAMPP, T. (1999) Maximizing Text-Mining Performance. IEEE, 63-69.
35. WIEMER-HASTINGS, P. & ZIPITRIA, I. (2000) Rules for Syntax, Vectors for Semantics. Edinburgh, University of Edinburgh.
36. WITTEN, I. H. & FRANK, E. (2005) Data Mining: Practical Machine Learning Tools and Techniques, San Francisco, Morgan Kaufmann Publishers.
37. WONG, P., COWLEY, W., FOOTE, H., JURRUS, E. & THOMAS, J. (2000) Visualizing Sequential Patterns for Text Mining. IEEE Synposium on Information Visualisation 2000 (InfoVis'00).
38. WU, H. & GUNOPLOUS, D. (2002) Evaluating the Utiliy of Statistical Phrases and Latent Semantic Indexing for Text Classification. IEEE, 713-716.
39. YANG, H. & LEE, C. (2003) A Text Mining Approach on Automatic Generation of Web Directories and Hierarchies. IEEE/WIC International Conference on Web Intelligence (WI'03). IEEE.
40. YU, J. (2005) General C-Means Clustering Model. IEEE Computer Society, 1197-1211.
ZELIKOVITZ, S. & HIRSH, H. (2000) Using LSI for Text Classification in the Presence of Background Text. Piscataway, New Jersey, Rutgers University.
41. ZHONG, M., CHEN, Z. & LIN, Y. (2004) Using Classification and key phrase Extraction for information retrieval. IEEE, 3037-3041.
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