A Knowledge Discovery based on Synaptic data discovery Model

Web Mining is an extraction of knowledge from web data. Several data get generated while working with web usage. Analyzing such data and finding the usable entity to provide a better user experience can be an advantage of algorithms. Thus a knowledge discovery and providing quick solutions to the input query can be performed. The previous author performs many approaches for data analysis, weight analysis, and data processing. TF-IDF, Semantic, FP growth algorithm, and such other techniques are used by previous research for knowledge analysis. In this research, an advance synaptic data discovery model for web data extraction and analysis is performed. The proposed algorithm works with the tree architecture-based discovery and enables finding the relevant terminology. Thus finding a better solution for the prediction and finding a better knowledge query output is performed. The experiment result shows the effectiveness of the proposed approach over the traditional algorithm.