Frequent Item set Mining of Big Data for Social Media

Big data is a term for massive data sets having large, more varied and complex structure with the difficulties of storing, analyzing and visualizing for further processes or results. Bigdata includes data from email, documents, pictures, audio, video files, and other sources that do not fit into a relational database. This unstructured data brings enormous challenges to Bigdata.The process of research into massive amounts of data to reveal hidden patterns and secret correlations named as big data analytics. Therefore, big data implementations need to be analyzed and executed as accurately as possible. The proposed model structures the unstructured data from social media in a structured form so that data can be queried efficiently by using Hadoop MapReduce framework. The Bigdata mining is essential in order to extract value from massive amount of data. MapReduce is efficient method to deal with Big data than traditional techniques.The proposed Linguistic string matching Knuth-Morris-Pratt algorithm and K-Means clustering algorithm gives proper platform to extract value from massive amount of data and recommendation for user.Linguistic matching techniques such as Knuth–Morris–Pratt string matching algorithm are very useful in giving proper matching output to user query. The K-Means algorithm is one which works on clustering data using vector space model. It can be an appropriate method to produce recommendation for user