The assessment of financial credit risk is an important and challenging research topic in the
area of accounting and finance. Numerous efforts have been devoted into this field since the …

Cluster ensembles have been shown to be better than any standard clustering algorithm at
improving accuracy and robustness across different data collections. This meta-learning …

Due to its ability to combine multiple base clusterings into a probably better and more robust
clustering, the ensemble clustering technique has been attracting increasing attention in …

As a promising way for heterogeneous data analytics, consensus clustering has attracted
increasing attention in recent decades. Among various excellent solutions, the co …

A clustering ensemble aims to combine multiple clustering models to produce a better result
than that of the individual clustering algorithms in terms of consistency and quality. In this …

Clustering algorithms are widely used in many societal resource allocation applications,
such as loan approvals and candidate recruitment, among others, and hence, biased or …

Ensemble clustering, also known as consensus clustering, is emerging as a promising
solution for multi-source and/or heterogeneous data clustering. The co-association matrix …

K-means is a fundamental clustering algorithm widely used in both academic and industrial
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …

Cluster analysis and outlier detection are two continuously rising topics in data mining area,
which in fact connect to each other deeply. Cluster structure is vulnerable to outliers; …

The aim of clustering ensemble is to combine multiple base partitions into a robust, stable
and accurate partition. One of the key problems of clustering ensemble is how to exploit the …