Background In recent years, the introduction of single-cell RNA sequencing (scRNA-seq)
has enabled the analysis of a cell's transcriptome at an unprecedented granularity and …

Existing data mining techniques, more particularly iterative learning algorithms, become
overwhelmed with big data. While parallelism is an obvious and, usually, necessary …

Iterative machine learning algorithms, ie, k-means (KM), expectation maximization (EM),
become overwhelmed with big data since all data points are being continually and …

In many governments and private institutions, one of the major tasks is to select the best
project proposals for allocating the fund. These funding organizations select the proposals …

The EM algorithm is one of many important tools in the field of statistics. While often used for
imputing missing data, its widespread applications include other common statistical tasks …

Background: In recent years, the introduction of single-cell RNA sequencing (scRNA-seq)
has enabled the analysis of a cell's transcriptome at an unprecedented granularity and …

Since software fault detection process is well-modeled by a non-homogeneous Poisson
process, it is of great interest to estimate accurately the intensity function from observed …

Stellar data, only a few years ago, measured in the. 1M of objects. Now, sets are routinely
1M. With the launch of ESA's Gaia in 2013, we expect 1000M stellar objects measured more …

Without question, astronomy is about Big Data and clustering is a very common task over
astronomy domain. The expectation-maximization algorithm is among the top 10 data …