This chapter presents classical grid-based clustering algorithms as well as those algorithms
that directly address the challenges of nonuniformity, locality, and high-dimensionality. Grid …

Various applications, such as electronic business, satellite remote sensing, intrusion
discovery, and network traffic monitoring, generate large unbounded data stream sequences …

Since the notion of hesitant fuzzy set was introduced, some clustering algorithms have been
proposed to cluster hesitant fuzzy data. Beside of hesitation in data, there is some hesitation …

Clustering techniques can group genes based on similarity in biological functions. However,
the drawback of using clustering techniques is the inability to identify an optimal number of …

It is expected for new clustering algorithms to find the appropriate number of clusters when
dealing with complex data, meaning various shapes and densities. They also have to be self …

In tourism, the phenomenon of spatial agglomeration (concentration of economic activity)
spreads beyond the borders of the territorial units. It is referred to as geographic 'spillovers' …

Data clustering aims at partitioning the data set into several disjoint segments, where the
data inside the same segment are closely related, and the ones between two different …

Алгоритмы кластеризации в задачах сегментации спутниковых изображений – тема
научной статьи по компьютерным и информационным наукам читайте бесплатно …

Most gear fault diagnosis (GFD) approaches suffer from inefficiency when facing with
multiple varying working conditions at the same time. In this paper, a non-negative matrix …

The grid-based clustering algorithm, which partitions the data space into a finite number of
cells to form a grid structure and then performs all clustering operations on this obtained grid …