Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R.

Big data clinical research typically involves thousands of patients and there are numerous variables available. Conventionally, these variables can be handled by multivariable regression modeling. In this article, the hierarchical cluster analysis (HCA) is introduced. This method is used to explore similarity between observations and/or clusters. The result can be visualized using heat maps and dendrograms. Sometimes, it would be interesting to add scatter plot and smooth lines into the panels of the heat map. The inherent R heatmap package does not provide this function. A series of scatter plots can be created using lattice package, and then background color of each panel is mapped to the regression coefficient by using custom-made panel functions. This is the unique feature of the lattice package. Dendrograms and color keys can be added as the legend elements of the lattice system. The latticeExtra package provides some useful functions for the work.

The undecimated wavelet decomposition and its reconstruction.

This paper describes the undecimated wavelet transform and its reconstruction. In the first part, we show the relation between two well known undecimated wavelet transforms, the standard undecimated wavelet transform and the isotropic undecimated wavelet transform. Then we present new filter banks specially designed for undecimated wavelet decompositions which have some useful properties such as being robust to ringing artifacts which appear generally in wavelet-based denoising methods. A range of examples illustrates the results.

Wavelet-based combined signal filtering and prediction.

We survey a number of applications of the wavelet transform in time series prediction. We show how multiresolution prediction can capture short-range and long-term dependencies with only a few parameters to be estimated. We then develop a new multiresolution methodology for combined noise filtering and prediction, based on an approach which is similar to the Kalman filter. Based on considerable experimental assessment, we demonstrate the powerfulness of this methodology.

A wavelet, fourier, and PCA data analysis pipeline: application to distinguishing mixtures of liquids.

Using a new optical engineering technique for the "fingerprinting" of beverages and other liquids, we study and evaluate a range of features. The features are based on resolution scale, invariant frequency information, entropy, and energy. They allow mixtures of beverages to be very precisely placed in principal component plots used for the data analysis. To show this we make use of data sets resulting from optical/near-infrared and ultrasound sensors. Our liquid "fingerprinting" is a relatively open analysis framework in order to cater for different practical applications, in particular, on one hand, discrimination and best fit between fingerprints, and, on the other hand, more exploratory and open-ended data mining.

Gray and color image contrast enhancement by the curvelet transform.

We present in this paper a new method for contrast enhancement based on the curvelet transform. The curvelet transform represents edges better than wavelets, and is therefore well-suited for multiscale edge enhancement. We compare this approach with enhancement based on the wavelet transform, and the Multiscale Retinex. In a range of examples, we use edge detection and segmentation, among other processing applications, to provide for quantitative comparative evaluation. Our findings are that curvelet based enhancement out-performs other enhancement methods on noisy images, but on noiseless or near noiseless images curvelet based enhancement is not remarkably better than wavelet based enhancement.