Distributed Fuzzy Rough Set for Big Data Analysis in Cloud Computing Fuzzy rough set based feature selection is a widely adopted technique for big data analysis. However, the high accuracy of this technique depends on all the data correlations, so that it always works in the centralized computing mode. With the increasing data volume, the centralized server, especially its computation capability and memory space, cannot afford the computing of fuzzy rough set. To enable the fuzzy rough set for big data analysis, in this paper, we propose the novel Distributed Fuzzy Rough Set (DFRS) based feature selection in cloud computing, which separates and assigns the tasks to multiple nodes for parallel computing. The key challenge is to maintain the global information on each distributed node without conserving the entire fuzzy relation matrix. We tackle this challenge by a dynamic data decomposition algorithm and a data summarization process on each distributed node. Extensive experiments based on multiple real datasets demonstrate that DFRS significantly improves the runtime and its feature selection accuracy is nearly the same as the traditional centralized computing.

Efficient Attribute Reduction with Submodular Function Optimization Attribute Reduction problem is an important content in Rough Set theory, which has been proven to be NPhard. It tends to find a subset of attributes that preserve the same discriminatory power as the entire attributes. Heuristics methods are used to find the minimum attribute subset. In this paper, we use three typical heuristic methods to do Attribute Reduction. They are positiveregionbased, discernibilitymatrixbased and mutualinformationbased Attribute Reduction algorithm. Experimental results validate that all the three algorithms have good performance on classification for symbolic data. However, the computational complexity of the three Attribute Reduction algorithms are all O(n2), so it s hard to apply them to massive data. In order to improve the efficiency of the three Attribute Reduction algorithms, we remodel the objective of them from the perspective of Submodular Function Optimization with submodular optimization strategies. We first optimize the attribute selection process with LazyGreedy strategy and then extend the serial algorithm to a parallel version according to the parallel optimization strategy of submodular functions. Experimental results validate that the efficiency of the proposed Attribute Reduction algorithms.