Data Mining, Machine Learning and Big Data Analytics

Cognitive Analytics: Going Beyond Big Data Analytics and Machine Learning

This chapter defines analytics and traces its evolution from its origin in 1988 to its current stage—cognitive analytics. We discuss types of learning and describe classes of machine learning algorithms. Given this backdrop, we propose a reference architecture for cognitive analytics and indicate ways to implement the architecture. A few cognitive analytics applications are briefly described. T...

“Social Media and Forced Displacement: Big Data Analytics & Machine-Learning”

Machine Learning Big Data Framework and Analytics for Big Data Problems

Generally, big data computing deals with massive and high dimensional data such as DNA microrray data, financial data, medical imagery, satellite imagery and hyperspectral imagery. Therefore, big data computing needs advanced technologies or methods to solve the issues of computational time to extract valuable information without information loss. In this context, generally, Machine Learning (M...

A Fuzzy TOPSIS Approach for Big Data Analytics Platform Selection

Big data sizes are constantly increasing. Big data analytics is where advanced analytic techniques are applied on big data sets. Analytics based on large data samples reveals and leverages business change. The popularity of big data analytics platforms, which are often available as open-source, has not remained unnoticed by big companies. Google uses MapReduce for PageRank and inverted indexes....

Big Data Analytics in Bioinformatics: A Machine Learning Perspective

Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big data using the distributed and parallel computing technologies. Usually big data tools perform computation in batch-mode and are not optimized for iterative pr...