Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …

Daily inflow forecasts provide important decision support for the operations and
management of reservoirs. Accurate and reliable forecasting plays an important role in the …

The K-means algorithm is among the most commonly used data clustering methods.
However, the regular K-means can only be applied in the input space, and it is applicable …

We initiate a comprehensive experimental study of objective-based hierarchical clustering
methods on massive datasets consisting of deep embedding vectors from computer vision …

At the dawn of the Internet era graph analytics play an important role in high-and low-level
network policymaking across a wide array of fields so diverse as transportation network …

The content of free calcium oxide (f-CaO) in cement clinker is a key indicator for testing the
quality of cement clinker. To address the problem that the content of f-CaO cannot be …

The method of random Fourier features (RFF), proposed in a seminal paper by Rahimi and
Recht (NIPS'07), is a powerful technique to find approximate low-dimensional …

Using random projection, a method to speed up both kernel k-means and centroid
initialization with k-means++ is proposed. We approximate the kernel matrix and distances …

This study proposes a so-called new fuzzy subtractive clustering (NFSC) algorithm to
allocate orders to the appropriate hub with three criteria, namely, travelling distance, delivery …

In the field of data mining, clustering is the technique of grouping millions of data points to
form clusters. Data of the same class are grouped together. K-Means clustering is the most …