The prevalence of mobile phones, the internet-of-things technology, and networks of
sensors has led to an enormous and ever increasing amount of data that are now more …

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 …

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

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 …

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; …

Data-driven modeling usually suffers from data sparsity, especially for large-scale modeling
for urban phenomena based on single-source urban infrastructure data under fine-grained …

Exploring cellphone network data has been proved to be a very effective way to understand
urban populations because of the high penetration rate of cellphones. However, the state-of …

Computational Auditory Scene Analysis (CASA) is typically achieved by statistical models
trained offline on available data. Their performance relies heavily on the assumption that the …

Consensus clustering fuses diverse basic partitions (ie, clustering results obtained from
conventional clustering methods) into an integrated one, which has attracted increasing …

Hyperspectral band selection is a commonly used technique to alleviate the curse of
dimensionality. Recently, clustering-based methods have attracted much attention for their …