Now showing items 1-2 of 2
| Abstract: | For years, choosing the right career by monitoring the trends and scope for different career paths have been a requirement for all youngsters all over the world. In this paper we provide a scientific, data mining based method for job absorption rate prediction and predicting the waiting time needed for 100% placement, for different engineering courses in India. This will help the students in India in a great deal in deciding the right discipline for them for a bright future. Information about passed out students are obtained from the NTMIS ( National technical manpower information system ) NODAL center in Kochi, India residing in Cochin University of science and technology |
| Description: | International Journal of Recent Trends in Engineering, Vol. 1, No. 1, May 2009 |
| URI: | http://dyuthi.cusat.ac.in/purl/4089 |
| Files | Size |
|---|---|
| Applying Data m ... s for Career Selection.pdf | (362.7Kb) |
| Abstract: | Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining |
| Description: | International Journal of Advanced Computing, ISSN:2051-0845, Vol.36, Issue.1 |
| URI: | http://dyuthi.cusat.ac.in/purl/4105 |
| Files | Size |
|---|---|
| A Novel Decisio ... c Datasets - C 4.5Stat.pdf | (183.3Kb) |
Now showing items 1-2 of 2
Dyuthi Digital Repository Copyright © 2007-2011 Cochin University of Science and Technology. Items in Dyuthi are protected by copyright, with all rights reserved, unless otherwise indicated.