Text Clustering Algorithm, The Projection Pursuit Model

The efficient and high quality Text Clustering Algorithms would help to discover and mine the huge latent valued knowledge from a great deal of unstructured text sources. Vector Space Model is usually used to express text feature with high dimensional characteristic.Applying the Projection Pursuit Model in text feature dimension reduction to project high dimensional feature vector into visualization space with two or three dimension. It not only can express text structure features, but also reduce computation complexity, improve efficiency and precision of the text clustering algorithms. The key in this process is to find the global optimal projecting directions.This paper proposed two kinds of improved genetic algorithm based projection pursuit text clustering algorithm, which uses accelerating immune genetic algorithm to determine optimal projection direction and project the high-dimensional text feature vectors into two or three dimensional space. It can merge text structure features in a visualization space, and determine the text cluster number intuitionisticly. Experiments demonstrate this algorithm can get better clusting result.