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Minig rules of concept drift using genetic algorithm

Vivekanandan P., Nedunchezhian R.

Journal of Artificial Intelligence and Soft Computing Research

2011

Vol. 1, No. 2

135--145

In a database the data concepts changes over time and this phenomenon is called as concept drift. Rules of concept drift describe how the concept changes and sometimes they are interesting and mining those rules becomes more important. CDR tree algorithm is currently used to identify the rules of concept drift. Building a CDR tree becomes a complex process when the domain values of the attributes get increased. Genetic Algorithms are traditionally used for data mining tasks. In this paper, a Genetic Algorithm based approach is proposed for mining the rules of concept drift, which makes the mining task simpler and accurate when compared with the CDR-tree algorithm.

Impulsive noise cleaning by smoothing of images using fast median method (FMM)

Velu C. M., Prabakaran N., Vivekanandan P.

Image Processing & Communications

2008

Vol. 13, no 3-4

19-30

The objective of this paper is to propose new approaches FMM for removing impulsive noise in images so as to smooth the image for further processing. The various methods like Laplacian filter. Gaussian filter. Mean filter, Mode filter, Maximum filter, Minimum filter, Hybrid filter, Adaptive filter, Low pass filter, High pass filter, Hybrid filter and Uniform filter are discussed. In this paper, a new approach is proposed to use the FMM, which is obtained by computing median in each row, again median is calculated for medians of each row of a convolution mask. In this method, for example, for a 3 x 3 mask, left and right gray values are loaded along with center pixel of each row or each column and these values are sorted in order. The new center value is considered as median value, with this new value, smoothing of an image is achieved. In this method, a smoothed image is computed by employing just an auxiliary function swap, which yields less computation time.