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 …

This paper synthesizes the results, methodology, and research conducted concerning the K‐
means clustering method over the last fifty years. The K‐means method is first introduced …

This paper proposes a k-means type clustering algorithm that can automatically calculate
variable weights. A new step is introduced to the k-means clustering process to iteratively …

Often considered more as an art than a science, the field of clustering has been dominated
by learning through examples and by techniques chosen almost through trial-and-error …

This paper presents a new k-means type algorithm for clustering high-dimensional objects in
sub-spaces. In high-dimensional data, clusters of objects often exist in subspaces rather …

This paper represents another step in overcoming a drawback of K-Means, its lack of
defense against noisy features, using feature weights in the criterion. The Weighted K …

While within-cluster information is commonly utilized in most soft subspace clustering
approaches in order to develop the algorithms, other important information such as between …

This paper proposes TW-k-means, an automated two-level variable weighting clustering
algorithm for multiview data, which can simultaneously compute weights for views and …

Prezentowana książka stanowi podsumowanie rozważań autora zawartych w wielu
opracowaniach dotyczących miary odległości, która została w pierwotnej wersji …

K-means is one of the most popular and widespread partitioning clustering algorithms due to
its superior scalability and efficiency. Typically, the K-means algorithm treats all features …