抽象的な

ACO Swarm Search Feature Selection for Data stream Mining in Big Data

Shivani Harde, Vaishali Sahare

Big data is a term for large datasets use for analysis to make beneficial decision and strategic move. But it has many technical challenges that also confront by both academic research communities and commercial IT deployment. Data streams and the curse of dimensionality are founded to be the root sources of Big Data. The commonly used procedure for data sourced from data streams is continuously making batch based model and inducing algorithms which is infeasible for real-time data mining. An optimal feature subset which is derived by mining over high dimensional data search space grows exponentially in size which leads to an intractable demand in computation. In order to solve this problem which is based on high dimensionality and streaming format in data feeds in big data , light weight feature selection is indicated which will particularly concentrate on mining data on fly , by using ant colony optimization (ACO) type of swarm search which can achieve enhanced analytical accuracy.