APPLICATIONS OF STATISTICAL DATA MINING METHODS

Data mining is a collection of analytical techniques to uncover new trends and patterns in large databases. These data mining techniques stress visualization to thoroughly study the structure of data and to check the validity of statistical model fit to the data and lead to knowledge discovery. Data mining is an interdisciplinary research area spanning several disciplines such as database management, machine learning, statistical computing, and expert systems. Although data mining is a relatively new term, the technology is not. Data mining allows users to analyze data from many different dimensions or angles, explore and categorize it, and summarize the relationships identified. Large investments in technology and data collection are currently being made in the area of precision agriculture, remote sensing, and in bioinformatics. Experiments conducted in these disciplines are generating mountains of data at a rapid rate. Analyzing such massive data combined with the biological and environmental information would not be possible without automated and efficient data mining techniques. Effective statistical and graphical data mining tools can enable agricultural researchers to perform quicker and more cost-effective experiments. Commonly used statistical and graphical data mining techniques in data exploration and visualization, model selection, model development, checking for violations of statistical assumptions, and model validation are presented here

Fracture mapping in challenging environment: a 3D virtual reality approach combining terrestrial LiDAR and high definition images

ArticleThis is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.The latest technological developments in computer vision allow the creation of georeferenced, non-immersive desktop virtual reality (VR) environments. VR uses a computer to produce a simulated three-dimensional world in which it is possible to interact with objects and derive metric and thematic data. In this context, modern geomatic tools enable the remote acquisition of information that can be used to produce georeferenced high-definition 3D models: these can be used to create a VR in support of rock mass data processing, analysis, and interpretation. Data from laser scanning and high quality images were combined to map deterministically and characterise discontinuities with the aim of creating accurate rock mass models. Discontinuities were compared with data from traditional engineering-geological surveys in order to check the level of accuracy in terms of the attitude of individual joints and sets. The quality of data collected through geomatic surveys and field measurements in two marble quarries of the Apuan Alps (Italy) was very satisfactory. Some fundamental geotechnical indices (e.g. joint roughness, alteration, opening, moisture, and infill) were also included in the VR models. Data were grouped, analysed, and shared in a single repository for VR visualization and stability analysis in order to study the interaction between geology and human activities.The authors gratefully acknowledge the assistance of the personal of the Romana Quarry and particularly Corniani M. This paper was possible because of support from the Tuscany Region Research Project known as “Health and safety in the quarries of ornamental stones—SECURECAVE”. The authors acknowledge Pellegri M and Gullì D (Local Sanitary Agency n.1, Mining Engineering Operative Unit—Department of Prevention) and Riccucci S (Centre of GeoTechnologies, University of Siena) for their support of this research

DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic