Dimension Debasing Towards Minimal Search Space Utilization for Mining Patterns in Big Data
Data mining algorithms generally produce patterns which are interesting. Such patterns can be used by domain experts in order to produce business intelligence. However, most of the existing algorithms that cannot properly work for uncertain data. Keeping uncertain data’s characteristics in mind, it can be said that they do have more search space with existing algorithms. In this paper, the authors proposed a method that can be used to reduce search space besides helping in producing patterns from uncertain data. The proposed method is based on MapReduce programming framework that works in distributed environment.