Publication Date


Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science


Blogs form an important source of information in today’s internet world. There are blogs on different topics such as technical, health, electronic gadgets, shopping, etc. However, most of the blog websites have the blogs arranged in chronological order rather than its contents. Such arrangement of blogs makes it difficult for the user searching information about a particular topic from the blog. To resolve this problem, we propose an approach to cluster the blogs based on its content. We studied several clustering algorithms available. The objective of this report is to understand various steps involved in clustering blog information and working of clustering algorithms which best fits in text clustering and improving clustering results by reducing its drawbacks. The report demonstrates a comparison between the K-Means, Vector Space Model (VSM), Latent Semantic Indexing (LSI), and Fuzzy C-Means (FCM) clustering algorithms discussed through the paper and selects the optimum algorithm for blog clustering. The paper proposes modification of selected optimum algorithm to get required blog clustering. A comparison table of the selected algorithm and modified algorithm is included at the end of the paper and the results showed the proposed modified algorithm has performed overall better compared to other clustering algorithms.