Portfolio formation in financial markets is the task of not taking non-necessary risks.
Quantitative investment powered by machine learning has opened many new opportunities …

Although k-means and its variants are known for their remarkable efficiency, they suffer from
a strong dependence on the prior knowledge of K and the assumption of a circle-like pattern …

Abstract Support Vector Clustering (SVC) is an important density-based clustering algorithm
which can be applied in many real world applications given its ability to handle arbitrary …

Clustering is one of the most important data mining techniques, its objective is to regroup
similar objects into groups, with the aim of maximizing the intra-cluster similarit and …

Support vector clustering (SVC) is a powerful algorithm for density-based clustering, offering
advantages such as handling arbitrary cluster shapes and determining the number of …

Clustering is one of the main data mining tasks with many proven techniques and successful
real-world applications. However, in changing environments, the existing systems need to …

In traditional focimeter measurements, the lens cannot completely coincide with the
diaphragm owing to the change of radius, resulting in an increase in the power …

Despite its remarkable capability in handling arbitrary cluster shapes, support vector
clustering (SVC) suffers from pricey storage of kernel matrix and costly computations …

In spite of with great advantage of discovering arbitrary shapes of clusters, support vector
clustering (SVC) is frustrated by large-scale data, especially on resource limited platform. It …

Concerning the good description of arbitrarily shaped clusters, collecting accurate support
vectors (SVs) is critical yet resource-consuming for support vector clustering (SVC). Even …