Selection criteria for text mining approaches

Computers in Human Behavior

Volumn 51, Issue , 2015, Pages 729-733

  Hashimi, Hussein ^a   Hafez, Alaaeldin ^a   Mathkour, Hassan ^a  

^a KING SAUD UNIVERSITY   (Saudi Arabia)

Author keywords

Classification; Clustering; Selection criteria; Text mining approaches

Indexed keywords

CLASSIFICATION (OF INFORMATION); INFORMATION RETRIEVAL; INFORMATION RETRIEVAL SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; TEXT PROCESSING; WORLD WIDE WEB;

APPROPRIATE TECHNIQUES; CLUSTERING; COLLECTION OF DOCUMENTS; DOCUMENT COLLECTION; SEARCH AND RETRIEVAL; SELECTION CRITERIA; TEXT MINING; TEXT MINING TECHNIQUES;

DATA MINING;

COMPARATIVE EFFECTIVENESS; INTERNET; MARKETING; MEDICINE; MINING; PUBLISHING; TELECOMMUNICATION;

EID: 84955517999     PISSN: 07475632     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chb.2014.10.062     Document Type: Article

References (33)

1. Achtert, E., Böhm, C., Kriegel, H. P., Kröger, P., Müller-Gorman, I., Zimek. Finding hierarchies of subspace clusters. LNCS: Knowledge discovery in databases: PKDD. Lecture Notes in Computer Science (Vol. 4213, pp. 446-453).
2. Ahmad, R., & Khanum, A. (2010). Document topic generation in text mining by using cluster analysis with EROCK. International Journal of Computer Science & Security (IJCSS), 4(2).
3. AlSumait, L., & Domeniconi, C. (2007). Text clustering with local semantic kernels.
4. Bhushan, J., Pushkar, W., Shivaji, K., & Nikhil, K. (2014). Searching research papers using clustering and text mining. International Journal of Emerging Technology and Advanced Engineering, 4(4).
5. Burley, D. (2010). Information visualization as a knowledge integration tool. International Journal of Knowledge Management Practice, 11(4).
6. Caropreso, M. F., Matwin, S., & Sebastiani, F. (2009). Statistical phrases in automated text categorization.
7. Dhawan, S., Singh, K., & Khanchi, V. (2004). A framework for polarity classification and emotion mining from text. International Journal of Engineering and Computer Science, 3(8).
8. Dhawan, S., Singh, K., & Khanchi, V. (2014). Emotion mining techniques in social networking sites. International Journal of Information & Computation Technology, 4(12), 1145-1153.
9. Don, A., Zheleva, E., Gregory, M., Tarkan, S., Auvil, L., Clement, T., et al. (2007). Discovering interesting usage patterns in text collections: Integrating text mining with visualization. In Proceedings of the 16th ACM conference on information and knowledge management (CIKM), November (pp. 213-222).
10. Gharehchopogh, F. S., & Abbasi Khalifehlou, Z. (2011). Study on information extraction methods from text mining and natural language processing perspectives. AWER Procedia information technology & computer science, 2nd world conference on information technology.
11. Gupta, V. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1(1) (August).
12. Howland, P., & Park, H. (2007). Cluster-preserving dimension reduction methods for document classification.
13. Kiyavitskaya, N., Zeni, N., Mich, L., Cordy, J., & Mylopoulos, J. (2006). Text mining through semi automatic semantic annotation. In Proceedings of Practical Aspects of Knowledge Management (PAKM'06), Vol. 4333 of LNCS, (pp. 143-154).
14. Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31, 249-268.
15. Krause, A., Leskovec, J., & Guestrin, C. (2006). Data association for topic intensity tracking. In International conference on machine learning (ICML) (pp. 497-504).
16. Kriegel, H. P., Kröger, P., & Zimek, A. (2009). Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. Transactions on Knowledge Discovery from Data (New York, NY: ACM), 3(1), 1-58.
17. Lehmam, A. (2010). Essential summarizer: Innovative automatic text summarization software in twenty languages - ACM digital library. Published in proceeding RIAO'10 adaptively, personalization and fusion of heterogeneous information, CID Paris, France.
18. Lincy Liptha, R., Raja, K., & Tholkappia Arasu, G. (2010). Text clustering using concept-based mining model. International Journal of Electronics and Computer Science Engineering (ISSN: 2277-1956).
19. Navaneethakumar, V. M., & Chandrasekar, C. (2012). A consistent web documents based text clustering using concept based mining model. IJCSI International Journal of Computer Science Issues, 9(4) (No. 1).
20. Patel, R., & Sharma, G. (2014). A survey on text mining techniques. International Journal of Engineering and Computer Science, 3(5).
21. Rath, S. K., Jena, M. K., Nayak, T., & Bisoyee, B. (2011). Data mining: A healthy tool for your information retrieval and text mining. International Journal of Computer Science and Information Technologies, 2.
22. Scott, S., & Matwin, S. (2011). Feature engineering for text classification.
23. Senellart, P., & Blondel, V. D. (2008). Automatic discovery of similar words, survey of text mining II: Clustering, classification and retrieval. Springer-Verlag (pp. 25-44).
24. Shelke, N. M. (2014). Approaches of emotion detection from text. International Journal of Computer Science and Information Technology Research, 2(2), 123-128.
25. Silwattananusarn, T., & Tuamsuk, K. (2012). Data mining and its applications for knowledge management: A literature review from 2007 to 2012. International Journal of Data Mining & Knowledge Management Process, 2(5).
26. Tan, H., & Lambrix, P. (2009). Selecting an ontology for biomedical text mining. Human language technology conference. BioNLP Workshop Association for Computational Linguistics.
27. Vashishta, S., & Jain, Y. K. (2011). Efficient retrieval of text for biomedical domain using data mining algorithm. International Journal of Advanced Computer Science and Applications, 2(4).
28. Velmurugan, T., & Santhanam, T. (2010). Performance evaluation of K-means and fuzzy C-means clustering algorithms for statistical distributions of input data points. European Journal of Scientific Research, 46(3)320-330 (ISSN 1450-216X).
29. Wang, X., Wang, J. T. L., Lin, K. I., Shasha, D., Shapiro, B. A., & Zhang, K. (1999). An index structure for data mining and clustering.
30. Wu, S. T., Li, Y., & Xu, Y. (2006). Deploying approaches for pattern refinement in text mining. In Proceedings of the sixth international conference on data mining.
31. Yassine, M., & Hajj, H. (2010). A framework for emotion mining from text in online social networks. In IEEE international conference on data mining workshops (pp. 1136-1142). Sydney, NSW: IEEE publications.
32. Zhai, C., Velivelli, A., & Yu, B. (2004). A cross collection mixture model for comparative text mining. Proceedings of ACM KDD 2004 (KDD'04) (pp. 743-748).
33. Zhou, Y., Zhang, Y., Vonortas, N., & Williams, J. (2012). A text mining model for strategic alliance discovery.