Data visualization and visual data mining are two crucial directions in data analysis domain, and both of which play pivotal roles in data exploring, patterns identifying, hypotheses validating, and interactive discovery. In this essay, we take the classic dataset Iris as an example to study the interconnectedness of the two. Firstly, the target of data mining is clearly analyzed, and then the Iris data set is visually explored for data dispersion distribution, data distribution frequency, data estimation probability density, and data key features and potential patterns. Secondly, research is focused on the data visualization of dispersion graph and KNN visual data mining, the interaction between Violin graph and decision tree, and the discovery of data mining algorithm models. Additionally, hypothesis validation for unsupervised clustering numbers in the Iris dataset is also explored. Through this essay, we aim to offer practical applications and insights into research methods pertinent to data visualization and visual data mining. We also hope to provide some application and reference for the research of data visualization and visualization mining.