Comparative Analysis of Unstructured Text Clustering Techniques
In recent times due to the increase of internet growth and technological advancement most of the data generated are of unstructured text data. Text mining plays an important role in information retrieval. Therefore the unsupervised learning technique like clustering is mainly applicable for unstructured text data. To improve the quality of information retrieval text clustering gives precise mining by organizing clusters of similar documents from huge collection. It is a current challenge to explore meaningful and compact insights from massive collections of the unstructured documents. Although there exists a lot of text clustering technologies, most of them are not scalable and clusters provided are not that much efficient to give desired result, and it leads to huge computation time. This paper gives the detailed comparative analysis of different clustering techniques which improves the precision of retrieval accuracy and leads to efficient clusters.