Analysis of proteomics data: Block $k$-mean alignment

Optimum Block Size in Separate Block Bootstrap to Estimate the Variance of Sample Mean for Lattice Data

The statistical analysis of spatial data is usually done under Gaussian assumption for the underlying random field model. When this assumption is not satisfied, block bootstrap methods can be used to analyze spatial data. One of the crucial problems in this setting is specifying the block sizes. In this paper, we present asymptotic optimal block size for separate block bootstrap to estimate the...

Comparative Analysis of Hybrid K-Mean Algorithms on Data Clustering

Data clustering is a process of organizing data into certain groups such that the objects in the one cluster are highly similar but dissimilar to the data objects in other clusters. K-means algorithm is one of the popular algorithms used for clustering but k-means algorithm have limitations like it is sensitive to noise ,outliers and also it does not provides global optimum results. To overcome...

k-equitable mean labeling

Clustering of Data Using K-Mean Algorithm

Clustering is associate automatic learning technique geared toward grouping a collection of objects into subsets or clusters. The goal is to form clusters that are coherent internally, however well completely different from one another. In plain words, objects within the same cluster ought to be as similar as potential, whereas objects in one cluster ought to be as dissimilar as potential from ...

Proteomics: Analysis of Spectral Data

The goal of disease-related proteogenomic research is a complete description of the unfolding of the disease process from its origin to its cure. With a properly selected patient cohort and correctly collected, processed, analyzed data, large scale proteomic spectra may be able to provide much of the information necessary for achieving this goal. Protein spectra, which are one way of representi...