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In this research, we propose the table based KNN as the approach to the text categorization. In previous works, we discovered that encoding texts into tables improved the performance in the text categorization, so in this research, become to consider the possibility of encoding words into tables as well as texts. In this research, we encode words into tables where entries are texts and their weights,...
This research proposes the table based AHC algorithm as the approach to the word clustering task. The results from encoding texts into tables were successful in the previous works on the text categorization and the text clustering, and if oppositely to the case of the text encoding, texts are assumed to be elements of each word, it becomes to be possible to encode words into tables. In this research,...
In this research, we propose the similarity matrix based version of NTSO as the approach to the text clustering. For using one of traditional approaches to text clustering, documents should be encoded into numerical vectors; encoding so causes the two main problems: the huge dimensionality and the sparse distribution. In order to solve the problems, in this research, we propose to encode documents...
A major problem with text classification problems is the high dimensionality of the feature space. This paper investigates how genetic algorithm and k-means algorithm can help select relevant features in text classification. which uses the genetic algorithm (GA) optimization features to implement global searching, and uses k-means algorithm to selection operation to control the scope of the search,...
High-dimensional feature space affects the quality and efficiency of text categorization. This paper investigates an improved genetic algorithm that how to help select relevant features in text classification. We follow the so-called "region growing" method to initialize the population, and uses k-means algorithm to selection operation to control the scope of the search, ensure the validity...
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