BIGDATA AND MACHINE LEARNING MODELS FOR DIMENTIONALITY REDUCTION PLATFORM

Various machine learning, cloud computing and big data models are most utilized control parameters in software applications. Therefore these systems are most demand at current trending decades. But these models are needs very low data access time, speed for process. Day to day life data storage servers and devices are costly and hardware complex, so dimensionality is increases with rapid manner. Any type of optimization techniques access takes more time consumption for high dimensional data. So, more applications related problems are occurs only at high dimensional data space does not at low space dimensional data. In this research proposes a dimensional reduction technique with Logistic regression (LR) model. This LR model is most helpful for dimensional reduction and clustering problems. LRML method has reduced the dimensional data size and achieved thee efficiency by 95.3% and ratio of reduction by 35.76%.