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 …

In this paper, we study what are the most important factors that deteriorate the performance
of the k-means algorithm, and how much this deterioration can be overcome either by using …

Partitioning a set of objects into homogeneous clusters is a fundamental operation in data
mining. The operation is needed in a number of data mining tasks. Clustering or data …

K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately,
due to its gradient descent nature, this algorithm is highly sensitive to the initial placement of …

We present a method for initialising the K-means clustering algorithm. Our method hinges on
the use of a kd-tree to perform a density estimation of the data at various locations. We then …

The quality of Wikipedia articles is debatable. On the one hand, existing research indicates
that not only are people willing to contribute articles but the quality of these articles is close …

Conventional k-means clustering is the widely used partitional method, mainly adapted to
machine learning and pattern recognition problems. This algorithm is highly sensitive to …

The performance of K-means and Gaussian mixture model (GMM) clustering depends on
the initial guess of partitions. Typically, clustering algorithms are initialized by random starts …

Scientists study trajectory data to understand trends in movement patterns, such as human
mobility for traffic analysis and urban planning. In this paper, we introduce a novel trajectory …

In this paper, a two-stage genetic clustering algorithm (TGCA) is proposed. This algorithm
can automatically determine the proper number of clusters and the proper partition from a …