Many existing clustering methods produce clusters with clear and sharp boundaries, which
does not truly reflect the fact that a cluster may not necessarily have a well-defined boundary …

The traditional k-means, which unambiguously assigns an object precisely to a single cluster
with crisp boundary, does not adequately show the fact that a cluster may not have a well-…

Similar to feature selection over completely labeled data, the aim of feature selection over
partially labeled data (semi-supervised feature selection) is also to find a feature subset which …

In this study, a granular ball based selector was developed for reducing the dimensions of
data from the perspective of attribute reduction. The granular ball theory offers a data-adaptive …

To address the difficulty of determining a clear-cut boundary of a cluster, three-way clustering
methods search for a new type of cluster structure characterized by a pair of a core region …

There are many incomplete data sets in all fields of scientific studies due to random noise,
data lost, limitations of data acquisition, data misunderstanding etc. Most of the clustering …

Clustering, as an unsupervised data mining technique, allows us to classify similar objects
into the same cluster according to certain criteria. It helps us identify patterns between objects, …

In the fields of rough set and machine learning, attribute reduction has been demonstrated
to be effective in removing redundant attributes with clear explanations. Therefore, not only …

Most existing results about attribute reduction are reported by considering one and only one
granularity, especially for the strategies of searching reducts. Nevertheless, how to derive …

By considering the information granulation in Granular Computing, the concept of the multi-granularity
is important. It is mainly because different results of information granulation will …