We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Survey of Object Oriented Mining for XML Data

by T.Sangeetha, G.Sophia Reena, T.Priya
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 16 - Number 4
Year of Publication: 2011
Authors: T.Sangeetha, G.Sophia Reena, T.Priya
10.5120/2002-2699

T.Sangeetha, G.Sophia Reena, T.Priya . Survey of Object Oriented Mining for XML Data. International Journal of Computer Applications. 16, 4 ( February 2011), 13-19. DOI=10.5120/2002-2699

@article{ 10.5120/2002-2699,
author = { T.Sangeetha, G.Sophia Reena, T.Priya },
title = { Survey of Object Oriented Mining for XML Data },
journal = { International Journal of Computer Applications },
issue_date = { February 2011 },
volume = { 16 },
number = { 4 },
month = { February },
year = { 2011 },
issn = { 0975-8887 },
pages = { 13-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume16/number4/2002-2699/ },
doi = { 10.5120/2002-2699 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:03:58.232136+05:30
%A T.Sangeetha
%A G.Sophia Reena
%A T.Priya
%T Survey of Object Oriented Mining for XML Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 16
%N 4
%P 13-19
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

According to the Petr Kuba, to adapt OR-FP for mining in XML data we preserve basic principles of the algorithm and modify only the input interface. To map XML data to our system we can use the following mapping: XML elements can be processed similarly to the objects in object-oriented data. The name of element corresponds to the class and the attributes of element correspond to the attributes of object. The content of the element (text nodes and elements) can be stored in a special attribute of the object. The type of this attribute should be a set or list – depending on whether we want to deal with an order of nodes. Some specifications (XPointer, XLink) add one more interesting feature to XML data – they allow us to use references to another documents or elements. We can represent this relation as simply as object references. Our proposal is to mining frequent pattern in collection of XML documents.

References
  1. Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. From Data Mining to Knowledge Discovery in Databases. AI Magazine 17, 3 (1996), 37–54.
  2. Famili, A., Shen, W.-M., Weber, R., and Simoudis, E. Data preprocessing and intelligent data analysis. Intelligent Data Analysis Journal 1, 1 (1997).
  3. Kramer, S., Lavraˇc, N., and Flach, P. Propositionalization approaches to relational data mining. In Relational Data Mining, S. Dˇzeroski and N. Lavraˇc, Eds. Springer-Verlag, September 2001, pp. 262–291.
  4. Domingos, P. Unifying instance-based and rule-based induction. Machine Learning 24, 2 (1996), 141–168.
  5. Agrawal, R., Imielinski, T., and Swami, A. N. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May 26–28, 1993 (1993), P. Buneman and S. Jajodia, Eds., ACM Press, pp. 207–216.
  6. Agrawal, R., and Srikant, R. Fast algorithms for mining association rules in large databases. In VLDB’94, Proceedings of 20th International Conference on Very Large Data Bases, September 12–15, 1994, Santiago de Chile, Chile (1994), J. B. Bocca, M. Jarke, and C. Zaniolo, Eds., Morgan Kaufmann, pp. 487–499.
  7. Muggleton, S. Inductive Logic Programming. New Generation Computing 8, 4 (1991), 295–318.
  8. Muggleton, S., and Raedt, L. D. Inductive logic programming: Theory and methods. Journal of Logic Programming 19/20 (1994), 629–679.
  9. Dehaspe, L., and Toivonen, H. Frequent query discovery: a unifying ILP approach to association rule mining. Tech. Rep. CW 258, Katholieke Univesiteit Leuven, Departmen of Computer Science, Celestijnenlaan 200A – B-3001 Heverlee (Belgium), March 1998.
  10. Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. Knowledge Discovery and Data Mining: Towards a Unifying Framework. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96) (1996), p. 82.
  11. Mannila, H., and Toivonen, H. An algorithm for finding all interesting sentences. In Proceedings of the 6th Internation Conference on Database Theory (1996), pp. 215–229.
  12. Dehaspe, L., and Raedt, L. D. Mining association rules in multiple relations. In ILP (1997), N. Lavrac and S. Dzeroski, Eds., vol. 1297 of Lecture Notes in Computer Science, Springer, pp. 125–132.
Index Terms

Computer Science
Information Sciences

Keywords

Object oriented data mining OR-FP