Present economy is largely dependent on the precise forecasting of the business avenues
using the stock market data. As the stock market data falls under the category of big data, the …

With the exponential growth of collected data in different fields like recommender system
(user, items), text mining (document, term), bioinformatics (individual, gene), co-clustering …

Difficulty in obtaining enough run-to-fail datasets is a major barrier that impedes the
widespread acceptance of Prognostic and Health Management (PHM) technology in many …

Clustering is intractable, so techniques exist to give a best approximation. Expectation
Maximization (EM), initially used to impute missing data, is among the most popular …

To deal with the unimaginable continual growth of data and the focus on its use rather than
its governance, the value of data has begun to deteriorate seen in lack of reproducibility …

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

The major strength of hierarchical clustering algorithms is that it allows visual interpretations
of clusters through dendrograms. Users can cut the dendrogram at different levels to get …

We review tasks and methods most relevant to Big Data analysis. Emphasis is made on the
conceptual and pragmatic issues of the tasks and methods (avoiding unnecessary …

The remarkable growth in online learning resources, educational software, the usage of
internet in the educational context, and the creation of national student information …

For studying cancer biology, multiomics data integration has become an invaluable tool.
With the amount of-omics data, its availability and the pace of its acquisition increasing …