Efficient Streaming Algorithm for Large Volume of Data

The volume of data is growing rapidly due to the usage of social sites like twitter, facebook etc. This data can be customer information data or ATM logs or Credit card data or telecom logs or any twitter or facebook data. This massive data is useful for business analyst for getting feedback about any product or service. The use of machine learning algorithms for analyzing market data will add more knowledge into the knowledge of investor. Traditional machine learning algorithms like C4.5 or CART have a limitation of memory size because they store all data on memory for building a model. So, these algorithms arenot suitable for large volume of data. These algorithms performs best if the size of data is small but if size of data increases the same algorithms shows poor results. In this paper, we have used Hoeffding tree for large volume of data and proved with results that Hoeffding tree performs best against other Machine learning algorithms. To increase accuracy we have used different classifiers at leaft level. We have collected dataset from twitter social site. Different phases of social media data mining are also explained in detail.