Almost all modern industries leverage data analytics to deal with various dimensions of their
business like demand forecasting, targeted marketing, and supply chain planning. In …

With the huge volume of data available as input, modern-day statistical analysis leverages
clustering techniques to limit the volume of data to be processed. These input data mainly …

Clustering is unsupervised classification of patterns (observations, data items, or feature
vectors) into teams (clusters). The drawback of clustering has been addressed in several …

In line with the technological developments, the current data tends to be multidimensional
and high dimensional, which is more complex than conventional data and need dimension …

Data clustering is an unsupervised method for extraction hidden pattern from huge data sets.
Having both accuracy and efficiency for high dimensional data sets with enormous number …

The current data tends to be more complex than conventional data and need dimension
reduction. Dimension reduction is important in cluster analysis and creates a smaller data in …

High dimensional data becomes very common with the rapid growth of data that has been
stored in databases or other information areas. Thus clustering process became an urgent …

Cluster analysis is an important high-level data mining procedure that can be used to
identify meaningful groups of objects within large data sets. Various dimension reduction …

Cluster interpretation after dimensionality reduction (DR) is a ubiquitous part of exploring
multidimensional datasets. DR results are frequently represented by scatterplots, where …

Social media being the most eminent source toward the growth of big data is important for
information retrieval‐based applications to improve the efficiency in proportional to the …