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This paper studies the imbalanced data classifycation problem and proposes bi-directional sampling based on clustering (BDSK) for the imbalanced data classification. This algorithm combines SMOTE over-sampling algorithm and under-sampling algorithm based on K-Means to solve the within-class imbalance problem and the between-class imbalance problem. It not only avoid induce too much noise but also...
Document Clustering is a widely studied problem in Text Categorization. It is the process of partitioning or grouping a given set of documents into disjoint clusters where documents in the same cluster are similar. K-means, one of the simplest unsupervised learning algorithms, solves the well known clustering problem following a simple and easy way to classify a given data set through a certain number...
This paper presents the results of classifying Arabic text documents using a decision tree algorithm. Experiments are performed over two self collected data corpus and the results show that the suggested hybrid approach of Document Frequency Thresholding using an embedded information gain criterion of the decision tree algorithm is the preferable feature selection criterion. The study concluded that...
Clustering techniques have been used by many intelligent software agents in order to retrieve, filter, and categorize documents available on the World Wide Web. Clustering is also useful in extracting salient features of related Web documents to automatically formulate queries and search for other similar documents on the Web. Traditional clustering algorithms either use a priori knowledge of document...
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