Sumam, Mary Idicula; Joseph, Alexander; Sudheep, Elayidom(Academy Publisher, May , 2009)
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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
Bindiya, Varghese M; Dr.Poulose Jacob, K; Dr.Unnikrishnan, A(Cochin University of Science And Technology, June 7, 2013)
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Abstract:
Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis
Description:
Department of Computer Science
Cochin University of Science and Technology