Traditional clustering partitions a group of objects into a number of nonoverlapping sets
based on a similarity measure. In real world, the boundaries of these sets or clusters may not …

Clustering is one of the most used tools in data analysis. In the last decades, due to the
increasing complexity of data, soft clustering has received a great deal of attention. There …

Clustering is one of the most widely used approaches in data mining with real life
applications in virtually any domain. The huge interest in clustering has led to a possibly …

In real cases, missing values tend to contain meaningful information that should be acquired
or should be analyzed before the incomplete dataset is used for machine learning tasks. In …

A new method of partitive clustering is developed in the framework of shadowed sets. The
core and exclusion regions of the generated shadowed partitions result in a reduction in …

As one of the essential topics in ensemble learning, a clustering ensemble is employed to
aggregate multiple base patterns to generate a single clustering output for improving …

Page 1 Studies in Computational Intelligence 152 S Jarosław Stepaniuk Rough - Granular
Computing in Knowledge Discovery and Data Mining R43 563 0404 R437 R438 Springer …

Intuitionistic fuzzy sets and rough sets are widely used for medical image segmentation, and
recently combined together to deal with uncertainty and vagueness in medical images. In …

Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical
image segmentation, and have recently been combined together to better deal with the …

Fuzzy sets, rough sets are efficient tools to handle uncertainty and vagueness in the medical
images and are widely used for medical image segmentation. Soft sets are a new …