Kasilingam Rajeswari

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The set of objects having same characteristics are organized in groups and clusters of these objects reformed known as Data Clustering. It is an unsupervised learning technique for classification of data. K-means algorithm is widely used and famous algorithm for analysis of clusters. In this algorithm, n number of data points are divided into k clusters(More)
Conventional approaches to visual search re-ranking empirically take the “classification performance” as the optimization objective, in which each visual document is determined relevant or not, followed by a process of increasing the order of relevant documents. First show that the classification performance fails to produce a globally optimal ranked list,(More)
This paper presents a novel feature selection based on association rule mining using reduced dataset. The key idea of the proposed work is to find closely related features using association rule mining method. Apriori algorithm is used to find closely related attributes using support and confidence measures. From closely related attributes a number of(More)
Clustering techniques have more importance in data mining especially when the data size is very large. It is widely used in the fields including pattern recognition system, machine learning algorithms, analysis of images, information retrieval and bio-informatics. Different clustering algorithms are available such as Expectation Maximization (EM), Cobweb,(More)
Text categorization is the task of automatically sorting a set of documents into categories from a pre-defined set. This means it assigns predefines categories to free-text documents. In this paper we are proposing a unique two stage feature selection method for text categorization by using information gain, principle component analysis and genetic(More)
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