A comparative analysis of algorithm for detecting concept-drift

In these day’s social media has become more popular. Many people are using social networking sites like Twitter, LinkedIn, Google+, Facebook etc., to be connect throughout the world for communicate with the families, business use or both. Among this Social Medias Twitter is one of the most popular social media people are using these days. They all collect huge amount of data day by day and serve as the source of Big Data analysis. In the Social media like Twitter there is always a drastic change in the trending topics depending on many parameters this is called concept drift. There is a mechanism or model to identify this concept drift so that we can find out what is the span of the trending tweets. In order to come out with the solution for this, we have organized one work as objectives: Collection of tweets, identifying the trending tweets, Algorithms to identify concept drift are ADWIN and Page-Hinkley test is used in this work and Comparative analysis of algorithms is done. After doing comparative analysis we got the accuracy of 83.24% and 80.06% on ADWIN algorithm and Page-Hinkley test algorithm respectively on our twitter dataset.