Effective dimensionality reduction by using soft computing method in data mining techniques

Apparently, there has been abundant of data generation and transfer going on over a daily basis. This data can either be static, dynamic or transactional in nature. There is frequent appending of new data to the data that is being already existing. There occurs a need to explore and fetch knowledge from the newly added data. One of the solution can be executing the algorithm for the appended datasets, which turns out to be quiet complicated and time engulfing. Resultant technique of dimensionality reduction has been proposed that aids in minimizing data dimensionality for carrying out various data processing processes like machine learning, data mining, pattern recognition and text retrieval in an effect and weather condition, best crop production is being analyzed in the existing research by making use of the proposed algorithm of decision tree (DT). At last the research work recommends a technique to handle various stages such as pre-manner. The method of dimensionality reduction had been proposed and incorporated in the soil and agriculture domain. The research work recommends principle component analysis (PCA) which is a dimension reduction algorithm employed in dynamic environment in order to produce reduced set of attribute as dynamic reduce thereby learning from it and drawing out future prediction for weather forecasting. The proposed method aids in assessment of new datasets pertaining to agriculture and soil upon its availability and makes appropriate modification in reduce so as it fits the whole dataset. On the basis of soil processing, dimensionality reduction and prediction via DT algorithm. The recommended PCA helps in comprehending data semantics, making the writing of analytics agriculture applications quiet simplistic and employing the approach of dimensional reduction for enhancing the performance. To successfully accomplish this, the accuracy of the predictions is carried out by dimensional reduction that is configured using a number of variables and inputs. The results yield no information loss with least execution time.