Dynamic Data Mining for Multidimensional Data Based On Machine Learning Algorithms

Abstract

While existing data mining methods and machine learning algorithms need a substantial quantity of data to train, more data must be acquired before they can be used. The intricacy of the model affects the size of the file. when analyzing data, like clinical trials, is difficult or costly, data-driven learning may be inefficient or overly basic It is important to have topic-specific subject knowledge throughout the concept-development process in biomedical research. Using current visualization techniques helps scale machine learning and visual data mining algorithms, much as it does with machine learning and visual data mining. Utilizing a new method, multidimensional data visualization is utilized to help the end consumer to better comprehend their data by incorporating machine learning and data mining. Enhancing model building efficacy via various data gathering techniques, such as variable data collection, data labeling, and data change is provided through these feedback designations. In theory, the greater array of techniques will make it possible to utilize lower sample sizes, which means it will be more relevant for larger data sets, which, if possible, may have a significant effect on certain circumstances where sample sizes are difficult to collect. The two applications involved in this experiment are both character recognition software: one to decipher characters' written content, and the other to determine the author's intended meaning (regression). In spite of this, it was shown that machine learning algorithms, with or without alternate data visualization, may provide similar results with less data.