Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …

Metal-organic framework (MOF) materials have the advantages of high specific surface area,
large pore volume and adjustable organizational structure. It has received widespread …

Feature selection is employed to reduce the feature dimensions and computational
complexity by eliminating irrelevant and redundant features. A vast amount of increasing …

Clustering is a fundamental data analysis method. It is widely used for pattern recognition,
feature extraction, vector quantization (VQ), image segmentation, function approximation …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system; neural networks provide a model …

Purpose The purpose of this paper is to propose a new RFM model called length, recency,
frequency, monetary and periodicity (LRFMP) for classifying customers in the grocery retail …

Unsupervised learning (clustering) deals with instances, which have not been pre-classified
in any way and so do not have a class attribute associated with them. The scope of applying …

In clustering analysis, data attributes may have different contributions to the detection of
various clusters. To solve this problem, the subspace clustering technique has been …

Bipartite spectral graph partition (BSGP) is a school of the most well-known algorithms
designed for the bipartite graph partition problem. It is also a fundamental mathematical …

Class imbalance problem has been extensively studied in the recent years, but imbalanced
data clustering in unsupervised environment, that is, the number of samples among clusters …