Feature Selection-Based Clustering on Micro-blogging Data

The growing popularity of micro-blogging phenomena opens up a flexible platform for the public as communication media for the public. For any trending/non-trending topic, thousands of post are posted daily in micro-blogs. During any important event, such as natural calamity and election, and sports event, such as IPL and World Cup, a huge number of messages (micro-blogs) are posted. Due to fast and huge exchange of messages causes information overload, hence clustering or grouping similar messages is an effective way to reduce that. Less content and noisy nature of messages are challenging factor in micro-blog data clustering. Incremental huge data is another challenge to clustering. So, in this work, a novel clustering approach is proposed for micro-blogs combining feature selection technique. The proposed approach has been applied to several experimental dataset, and it is compared with several existing clustering techniques which results in better outcome than other methods.