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 …

The integration of data and knowledge from several sources is known as data fusion. This
paper summarizes the state of the data fusion field and describes the most relevant studies …

Abstract Density Peak (DPeak) clustering algorithm is not applicable for large scale data,
due to two quantities, ie, ρ and δ, are both obtained by brute force algorithm with complexity …

K-means algorithm is the most commonly used simple clustering method. For a large
number of high dimensional numerical data, it provides an efficient method for classifying …

There is a large body of research on scalable machine learning (ML). Nevertheless, training
ML models on large, continuously evolving datasets is still a difficult and costly undertaking …

Approximate computing, where computation accuracy is traded off for better performance or
higher data throughput, is one solution that can help data processing keep pace with the …

The quality of K-Means clustering is extremely sensitive to proper initialization. The classic
remedy is to apply k-means++ to obtain an initial set of centers that is provably competitive …

Due to its simplicity and versatility, k-means remains popular since it was proposed three
decades ago. The performance of k-means has been enhanced from different perspectives …

Several algorithms for clustering data streams based on k-Means have been proposed in
the literature. However, most of them assume that the number of clusters, k, is known a priori …

Clustering is a key data mining task. This is the problem of partitioning a set of observations
into clusters such that the intra-cluster observations are similar and the inter-cluster …