Principal Component Analysis Visualizations in State Discovery by Animating Exploration Results

Visualization is a key point in data exploration. In this paper we have emphasis in adding dynamic features by constructing exploration animations. We use Principal Component Analysis (PCA) in dimensionality reduction and K-means clustering algorithm in defining states. In predicting state transitions, we use Hidden Markov Model (HMM). Analyzed physical data is got from self-healing autonomous data centers. Our research methodology is to animate state transitions for data exploration in modern computerized environment. We use Jupyter tool and Python 3 programming language in our experimental realization. As results we get PCA animations for exploration purposes. Our approach is based on state discovery, where it is possible to find some physical interpretations for the defined states and state transitions. State structure and behaviour depend strongly on analyzed data.