Clustering data has been of great interest to many researchers. Hierarchical clustering methods have been preferred because clusters can be visualized as a dendrogram. One of the problems of hierarchical clustering methods, however, is that the resulting dendrogram is not visually pleasing due to the scaling problem. Hence, a series of iterated logarithmic function is proposed so as to mitigate...

In the hierarchical clustering algorithms, it has become a basic difficult problem to determine the optimal clustering number in the dataset, as a result of the influence of outliers and noise points. Therefore, we propose a method to remove these interferential data in two stages in the hierarchical clustering algorithm, which is based on the traditional noise data removal method. Furthermore,...

The Urban spatial structure is affected by spatial interactions among various activity locations, and land uses in the city over the transportation system. Each city has its unique circulation pattern of passengers and freight due to its unique geographic conditions and the distribution of locations of economic activities. In that sense, it is claimed in this chapter per the authors that urban ...

Information about the state and planning of the speaker is obscured in traditional classifications of disfluencies which are generally at the word level. This study delves into the acoustic and prosodic information of repetitions, one of the most common disfluencies. A hierarchical clustering of prosodic features reveals three subsets of repetitions, each reflecting different problems in planning.

We create a weighted lexical network derived from the cosine similarities of financial news feeds to compare two clustering methods, Newman's Modularity method and hierarchical clustering. We find that hierarchical clustering, clustering documents according to shared unique terms, shows results that are closer to expectation.

Semi-analytical models of galaxy formation based on hierarchical clustering now make a wide range of predictions for observable properties of galaxies at low and high redshift. This article concentrates on 2 aspects: (1) Self-consistent modelling of dust absorption predicts a mean UV extinction AUV ∼ 1 mag, depending only weakly on redshift, and similar to observational estimates. (2) The model...

In this paper it is shown a process to demarcate areas with analogous wind conditions. For this purpose a dispersion graph between wind directions will be traced for all stations placed in the studied zone. These distributions will be compared among themselves using the hierarchical clustering algorithm. This information will be used to build a matrix, letting us work with all relations simulta...

the main purpose of this article is to present a clear picture of social bordering of urban space in mashhad and to identify the socio-spatial hierarchy in this city. 17510 resident householders were selected through stratified sampling and they were interviewed. the hierarchical clustering analysis was used to analyze the data, based on three social basic characteristics including occupational...

Hierarchical clustering is a common algorithm in data analysis. It unique among many algorithms that it draws dendrograms based on the distance of under certain metric, and group them. widely used all areas astronomical research, covering various scales from asteroids molecular clouds, to galaxies galaxy cluster. This paper systematically reviews history current status development hierarchical ...

Current multiple kernel clustering algorithms compute a partition with the consensus or graph learned from pre-specified ones, while emerging late fusion methods firstly construct partitions each separately, and then obtain one them. However, both of them directly distill information kernels graphs to matrices, where sudden dimension drop would result in loss advantageous details for clustering...