Parallel clustering is an important research area of big data analysis. The conventional
Hierarchical Agglomerative Clustering (HAC) techniques are inadequate to handle big-scale …

As a fundamental unsupervised learning task, hierarchical clustering has been extensively
studied in the past decade. In particular, standard metric formulations as hierarchical $ k …

Hierarchical clustering is a fundamental tool in data mining, machine learning and statistics.
Popular hierarchical clustering algorithms include top-down divisive approaches such as …

Hierarchical clustering is a classical method to provide a hierarchical representation for the
purpose of data analysis. However, in practical applications, it is difficult to deal with massive …

Single linkage (SLINK) hierarchical clustering algorithm is a preferred clustering algorithm
over traditional partitioning-based clustering as it does not require the number of clusters as …

在分布式计算和内存为王的时代, Spark 作为基于内存计算的分布式框架技术得到了前所未有的
关注与应用. 着重研究BIRCH 算法在Spark 上并行化的设计和实现, 经过理论性能分析得到并行 …

Average-linkage is one of the most popular hierarchical clustering algorithms. It is well
known that average-linkage does not scale to large data sets due to the slow asymptotic …

Entity Resolution (ER) in data integration systems is the problem of identifying groups of
tuples from one or multiple data sources that represent the same real-world entity. This is a …

The surge in data sizes in fluid processing applications necessitates partitioning the data
into clusters and studying their representatives instead of studying each voxel data point. In …

The major strength of hierarchical clustering algorithms is that it allows visual interpretations
of clusters through dendrograms. Users can cut the dendrogram at different levels to get …