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Real-world datasets are often vague and redundant, creating problem to take decision accurately. Very recently, Rough-set theory has been used successfully for dimensionality reduction but is applicable only on discrete dataset. Discretisation of data leads to information loss and may add inconsistency in the datasets. The paper aims at developing an(More)
Clustering is unsupervised learning where ideally class levels and number of clusters (K) are not known. K-clustering can be categorized as semi-supervised learning where K is known. Here we have considered K-Clustering with simultaneous feature selection. Feature subset selection helps to identify relevant features for clustering, increase(More)
Though DNA microarray technology simultaneously measures the expression levels of thousands of genes, only a few underlying gene features may account for significant data variation in gene classification problems. Selection of features from huge data set is difficult and so dimension reduction of gene expression data set is essential in order to determining(More)
Differential evolution (DE) algorithm is a population based stochastic search technique widely applied in scientific and engineering fields for global optimization over real parameter space. The performance of DE algorithm highly depends on the selection of values of the associated control parameters. Therefore, finding suitable values of control parameters(More)
Development of an efficient real time intrusion detection system (IDS) has been proposed in the paper by integrating Q-learning algorithm and rough set theory (RST). The objective of the work is to achieve maximum classification accuracy while detecting intrusions by classifying NSL-KDD network traffic data either 'normal' or 'anomaly'. Since RST processes(More)
Feature subset selection and dimensionality reduction of data are fundamental and most explored area of research in machine learning and data mining domains. Rough set theory (RST) constitutes a sound basis for data mining, can be used at different phases of knowledge discovery process. In the paper, by integrating the concept of RST and relational algebra(More)
Color image segmentation is one of the most difficult image processing tasks due to vagueness that often appear in the segmented images. Though type-1 fuzzy logic has been most intensively applied to color image segmentation but it fails to tackle all kind of uncertainties, especially when more robust segmented images are required. Introducing type-2 fuzzy(More)