Sequence Compulsive Incremental Updating of Knowledge in Learning Management Systems

1. http://moodle.org/ (last accessed December 10, 2011)

2. http://www.blackboard.com/ (last accessed December 10, 2011)

3. Romero, C., Ventura, S., Garcia, E.: Data Mining in Course Management Systems: Moodle Case Study and Tutorial. Computers and Education. Article in Press (2007)

   Google Scholar 

4. Clari, P.M., Arevalillo-Herraez, M., CeveronLleo, V.: Data Analysis as a Tool for Optimizing Learning Management Systems. In: Ninth IEEE International Conference on Advanced Learning Technologies (2009)

   Google Scholar 

5. Miranda, E.: Data Mining as a technique to analyze the learning styles of students in using the Learning Management System. In: SNATI 2011, June 17-18 (2011)

   Google Scholar 

6. Maloof, A.M., Michalski, R.S.: Incremental Learning with partial instance memory. Artificial Intelligence 154, 95–126 (2004)

   Article  MathSciNet  MATH  Google Scholar 

7. Piatestsky, S.U., Smyth, M.A., Uthurusamy, R.: Advances in Knowledge Discovery and Data mining. A text Book by AAAI/MIT Press (1996)

   Google Scholar 

8. Han, Kamber: Data Mining – Concepts and Techniques, 2nd edn. Elsevier (2006)

   Google Scholar 

9. Adhikari, A., Rao, P.R.: Synthesizing heavy association rules from different real data sources. Pattern Recognition Letters 29, 59–71 (2008)

   Article  Google Scholar 

10. Masseglia, F., Poncelet, P., Teisseire, M.: Incremental Mining of Sequential Patterns in Large Databases. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2004)

    Google Scholar 

11. Seeman, W.D., Michaski, R.S.: The CLUSTER3 System for goal-oriented conceptual clustering: method and preliminary results. WIT Transactions on Information and Communication Technologies 37 (2006)

    Google Scholar 

12. Michalski, R.S.: Knowledge Mining: A Proposed New Direction. In: Invited talk at the Sanken Symposium on Data Mining and Semantic Web, March 10-11. Osaka university, Japan (2003)

    Google Scholar 

13. Dubes, R.C., Jain, A.K.: Algorithms for Clustering data. Prentice Hall, Englewood Cliffs (1998)

    Google Scholar 

14. Jain, A.K., Murthy, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31(3) (September 1999)

    Google Scholar 

15. Li, Y., Dong, M., Hua, J.: Localized feature selection for clustering. Pattern Recognition Letters 29, 10–18 (2008)

    Article  Google Scholar 

16. Eric, C., Akanksha, B., Tig, C., Anhai, D., Jeffrey, N.: A Relational Approach to Incrementally Extracting and Querying Structure in Unstructured Data. In: Proceedings of the 33rd International Conference on VLDB, pp. 1045–1056 (2007)

    Google Scholar 

17. Fazli, C., Edward, A.F., Cory, D.S., Robert, K.F.: Incremental Clustering for Very Large Document Databases: Initial MARIAN Experience. An International Journal of Information Sciences—Informatics and Computer Science 84(1-2) (1995)

    Google Scholar 

18. Hammouda, K.M., Kamel, M.S.: Incremental Document Clustering Using Cluster Similarity Histograms. In: IEEE/WIC International Conference on Web Intelligence, Halifax, Canada, pp. 597–601 (October 2003)

    Google Scholar 

19. Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., Xu, X.: Incremental Clustering for Mining in a Data Warehousing Environment. In: Proceedings of the 24th VLDB Conference, New York (1998)

    Google Scholar 

20. Murthy, M.N., Sridhar, V.: A Knowledge-based Clustering algorithm. Pattern Recognition Letters 12, 511–517 (1991)

    Article  Google Scholar 

21. Chen, C.-Y., Hwang, S.-C., Oyang, Y.-J.: A Statistics-Based Approach to Control the Quality of Subclusters in Incremental Gravitational Clustering. Pattern Recognition 38(12), 2256–2269 (2005)

    Article  Google Scholar 

22. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 226–231. AAAI Press (1996)

    Google Scholar 

23. Zakir Ali, S., Nagabhushan, P., Pradeep Kumar, R.: Regression based Incremental Learning through Cluster Analysis of Temporal data. In: The 5th International Conference on Data Mining, DMIN 2009, Las Vegas, Nevada, USA, July 13-16, pp. 375–381 (2009)

    Google Scholar 

24. Nagabhushan, P., Zakir Ali, S., Pradeep Kumar, R.: A New Cluster-Histo-Regression Analysis for Incremental Learning from Temporal Data Chunks. International Journal of Machine Intelligence 02(01), 53–73 (2010)

    Google Scholar 

25. Nagabhushan, P., Zakir Ali, S., Pradeep Kumar, R.: Intelligent Methods of Fusing the Knowledge During Incremental Learning via Clustering in A Distributed Environment. International Journal of Advanced Networking and Applications 02(02), 581–596 (2010)

    Google Scholar 

26. Nagabhushan, P., Zakir Ali, S., Pradeep Kumar, R.: Incremental Updating of Knowledge Through Histo-Regression Lines using DBSCAN. In: The Bilateral Russian-Indian Scientific Workshop on Emerging Applications of Computer Vision, EACV 2011, Moscow, Russia, November 1-5 (2011)

    Google Scholar 

27. Nagabhushan, P., Gowda, K.C., Diday, E.: Dimensionality Reduction of Symbolic Data. Pattern Recognition Letters 16, 219–223 (1995)

    Article  Google Scholar 

28. Diday, E.: An Introduction to Symbolic Data and Analysis and the SODAS software. Journal of Symbolic Data Analysis (July 2002)

    Google Scholar 

29. Rangarajan, L.: Some New Approaches for Image Pattern Analysis Through Dimensionality Reduction. PhD Thesis of the University of Mysore, India (September 2004)

    Google Scholar 

30. Sanjay Pande, M.B.: An Algorithmic model for exploratory analysis of trace elements in cognition and recognition of neurological disorders; PhD thesis of the University of Mysore, India (2004)

    Google Scholar 

31. Pradeep Kumar, R., Nagabhushan, P.: An Approach Based on Regression Line Features for Low Complexity Content Based Image Retrieval. In: International Conference on Computing: Theory and Applications (ICCTA 2007), pp. 600–604 (2007)

    Google Scholar 

32. Gowda, S.D., Nagabhushan, P.: Equivalence between two document images through iterative histo analysis of the conventional entropy values. International Journal of System, Cybernetics and Informatics, 23–33 (October 2008)

    Google Scholar 

33. Pradeep Kumar, R., Nagabhushan, P.: An Approach Based on Regression Line Features for Low Complexity Content Based Image Retrieval. In: International Conference on Computing: Theory and Applications (ICCTA 2007), pp. 600–604 (2007)

    Google Scholar 

34. Kadampur, M.A., Somayajulu, D.V.L.N., Dhiraj, S.S.S., Satyam, S.G.P.: Privacy Preserving Clustering by Cluster Bulging for Information Sustenance. In: 4th International Conference on Information and Automation for Sustainability, ICIAFS 2008, pp. 240–246 (2008)

    Google Scholar 

35. Sayad, S., Balke, S.T., Sayad, S.: Scalable Data Mining. University of Toronto, http://chem-eng.utoronto.ca/~datamining/SDM/ (last accessed December 10, 2011)

36. Pradeep Kumar, R.: Wavelets for Knowledge Mining in Multi Dimensional Generic Databases. PhD Thesis of the University of Mysore (2006)

    Google Scholar 

37. http://ekluvya.adithyatech.edu.in/aitlms/ (last accessed December 10, 2011)

38. http://adithyatech.edu.in:8080/website/ (last accessed December 10, 2011)

Download references