In the last decades, a wide portfolio of Feature Weighting (FW) methods have been
proposed in the literature. Their main potential is the capability to transform the features in …

Subspace clustering (SC) is a promising technology involving clusters that are identified
based on their association with subspaces in high-dimensional spaces. SC can be classified …

Outlier detection is an important data analysis task in its own right and removing the outliers
from clusters can improve the clustering accuracy. In this paper, we extend the k-means …

A surge in the availability of data from multiple sources and modalities is correlated with
advances in how to obtain, compress, store, transfer, and process large amounts of complex …

Naive Bayes (NB) classifier ease of use along with its remarkable performance has led
many researchers to extend the scope of its applications to real-world domains by relaxing …

A massive amount of data is generated with the evolution of modern technologies. This high-
throughput data generation results in Big Data, which consist of many features (attributes) …

The superior interpretability and uncertainty modeling ability of Takagi-Sugeno-Kang fuzzy
system (TSK FS) make it possible to describe complex nonlinear systems intuitively and …

Discriminant analysis technique plays an important role in face recognition because it can
extract discriminative features to classify different persons. However, most existing …

Clustering algorithms with attribute weighting have gained much attention during the last
decade. However, they usually optimize a single-objective function that can be a limitation to …

Recently, many heterogeneous but related views of data have been generated in a number
of applications. Different views may represent distinct aspects of the same data, which often …