Localized matching model for plant prediction using incremental clustering

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Clustering is a data mining technique, which is used to place data elements into related groups without advance knowledge of the group definitions. Here, we propose an incremental clustering technique for managing knowledge in edaphology, a study concerned with the influence of soils on living things, particularly plants. The soil information along with the appropriate plants to be cultivated on it for better yield, collected by edaphologists, are utilized in the proposed system. Initially, an incremental DBSCAN algorithm is applied to a dynamic database where, the data may be frequently updated. Then, the data available in the soil database is grouped into clusters and every new element is added into it without the need of rerunning process. Finally, we have performed the plant prediction using regression model. The experimentation is carried out in soil database to analyze the performance of the proposed system in plant prediction.