Divisive Clustering method using Naive Bayes Algorithm for Text Categorization

Abstract: This research Divisive Clustering method using Naïve Bayes algorithm for text categorization has been developed to assigning an electronic document to one or more predefined categories or classes based on its textual context. In many information processing tasks, labels are usually expensive and the unlabeled data points are abundant. To reduce the cost on collecting labels, it is crucial to predict which unlabeled examples are the most informative, i.e., improve the classifier the most if they were labelled. Many active learning techniques have been proposed for text categorization, such as SVM Active and Transductive Experimental Design. However, most of previous approaches and researches are try to discover the discriminate structure of the data space, whereas the geometrical structure is not well respected. An agglomerative clustering algorithm has been implemented where the fixed M-dimensional static window has been replaced by a dynamic window scheme using Divisive Clustering Algorithm. Because of the independence assumption, the parameters for each attribute can be learned separately, and this greatly simplifies learning, especially when the number of attributes is large. The proposed scheme is experimented using Naive Bayes Algorithm with different data set to show its better effectiveness of text categorization in terms of minimum search time. The above mentioned algorithm has been implemented using Microsoft Visual Studio .NET 2008. The coding language used is C# .NET and the back end is MS SQL Server 2005.

Keywords: Text Mining, Automatic Text Categorization (ATC), Adaptive Active Learning Algorithm, Naïve Bayes Algorithm, Divisive Clustering Algorithm