Arpita Nagpal

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A review based on different types of clustering algorithms with their corresponding data sets has been proposed. In this paper, we have given a complete comparative statistical analysis of various clustering algorithms. Clustering algorithms usually employ distance metric or similarity metric to cluster the data set into different partitions. Well known(More)
This paper presents a clustering approach for grouping components of similar reusability using an already worked out fuzzy data set [2]. Research has shown that, component based systems development concept benefits the object oriented software development. A Component based system achieves flexibility by clearly separating the stable parts of systems from(More)
To reduce the dimensionality of dataset, redundant and irrelevant features need to be segregated from multidimensional dataset. To remove these features, one of the feature selection techniques needs to be used. Here, a feature selection technique to remove irrelevant features has been used. Correlation measures based on the concept of mutual information(More)
Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this(More)
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