Mining Text Data using different Text Clustering Techniques

摘要

Text mining is referred as text data mining or knowledge discovery from textual databases. The organization of text is a natural practice of humans and a crucial task for today’s vast databases. Clustering does this by assessing the similarity between texts and organizing them accordingly, grouping like ones together and separating those with different topics. Clusters provide a comprehensive logical structure that provides exploration, search and interpretation of current texts documents, as well as organization of future ones. Side information is available along with the text documents and may be of different kinds, which are embedded into the text document. However this sideinformation may be difficult to estimate. In such cases, it can be risky to include sideinformation into the mining process, because it can either increase the quality of the representation for the mining process. Therefore, so as to maximize the advantages from using this side information, to minimize the time complexity of clustering process and to remove impurity of clusters partition based text clustering techniques are used like kmeans & kWindows algorithm. Experimental results show that, KWindows clustering technique is giving better results as compared to Kmeans clustering technique and also shows that side information is effectively used for mining the data.