As the number of social networks users has increased day by day so has the user’s dependency for communication on the social networks. Social networks enable people to connect with one another in many different ways. Many social networks such as Twitter provide their users the functionality to tag the user’s current location to the post. This geographical information can be used in various information retrieval processes. Currently many methods are present which cluster the tweets using traditional K-means algorithm in which user has to specify the number of clusters to be formed, and if the tweets do not lie within those clusters they are then treated as outliers and discarded. This paper presents a framework which focuses on clustering and indexing of tweets on the basis of its geographical and temporal features. The X-means clustering has been used which does not require the cluster number input from the user but rather it takes input from the index of the specified characteristics created from tweets. The indexing mechanism will not only help in ease of searching but will also aid in many retrieval tasks. The experimental analysis shows that the proposed framework generates improved results over traditional tweet clustering methods.
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