Classification Based Neural Network Modelling with Continuous and Sequential data

Abstract

Data mining is a potential process that involves finding useful data from an existing database. The main core work of the paper is classification with Neural Networks and Pattern mining. The study is involved in figuring out the efficient and accurate algorithm from the existing mining algorithms. The sequential mining algorithms are incorporated in areas where the data are in the form of transactions. However, in this paper, the closed and frequent sequential mining algorithms are looked at deeper with the neural network Based Multi-Layer Perceptron to pick out a productive algorithm. There are a lot of potential sequential pattern mining algorithms that can be used on different types of data. And some are good with results when they are worked with certain datasets. The frequent sequential algorithms produce the data based on the ‘minsup’ value provided. Unlike the other mining algorithms, the closed pattern mining algorithms are compact and lossless when producing results. In the frequent sequential pattern mining category, the lattice-based CM-SPADE algorithm stands out with high accuracy in terms of memory management in megabytes and execution time in milliseconds. The CM-ClaSP algorithm performs better than the ClaSP, CloFAST and BIDE+ algorithms in the closed sequential pattern mining algorithm. Now the performance of the CM-SPADE has been added with the classification based format Multi-Layer Perceptron to boost up the Perceptron Networks Classification Style with an outcome of 93% of accuracy.