in this research, temporal and spatial behavior of siberian high (sh) was studied at the level of 1000 h.pa. this study has been done using daily data of geo potential height at 12 gmt in ncep/ncar database with spatial resolution of 2.5ã—2.5 degree in a 55 years period including 20089 days from january, 1st, 1951(dey, 11th, 1329) to december, 31st,2005(dey, 10th,1384). at first, the mean geo p...

This paper aims to conduct an analysis of the SARS-CoV-2 genome variation was carried out by comparing results clustering using several algorithms and distribution sequence in each cluster. The used are K-means, Gaussian mixture models, agglomerative hierarchical clustering, mean-shift DBSCAN. However, algorithm has a weakness grouping data that very high dimensions such as data, so dimensional...

– Partitioning clustering algorithms, construct non-overlapping clusters such that each item is assigned to exactly one cluster. Example: k-means – Agglomerative clustering algorithms construct a hierarchical set of nested clusters, indicating the relatedness between clusters. Example: hierarchical clustering • In classification, we partition data into known labels. For example, we might constr...

INTRODUCTION Patients with severe community-acquired pneumonia (CAP) are believed to have an exaggerated inflammatory response to bacterial infection. Therapies aiming to modulate the inflammatory response have been largely unsuccessful, perhaps reflecting that CAP is a heterogeneous disorder that cannot be modulated by a single anti-inflammatory approach. We hypothesize that the host inflammat...

Performing clustering analysis on a large amount of historical trajectory data can obtain information such as frequent flight patterns aircraft and air traffic flow distribution, which provide reference for the revision standard procedures optimization division airspace sectors. At present, most uses single algorithm. When other processing remains unchanged, it is difficult to improve effect by...

We present a method for refining clusters of 3D curves using a new coherence measure. Curves are common geometric models used for representing the information derived from different types of data, in particular scientific data represented as vector and tensor fields. Our method starts with an initial set of clusters. For each cluster, we probe samples from the cluster by “slicing” it with a pla...

Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measur...

Common document clustering algorithms utilize models that either divide a corpus into smaller clusters or gather individual documents into clusters. Hierarchical Agglomerative Clustering, a common gathering algorithm runs in O(n) to O(n) time, depending on the linkage of documents. In contrast, Bisecting K-Means Clustering has been shown to run in linear time with respect to the number of docum...

Hyperspectral images represent an important source of information to assess ecosystem biodiversity. In particular, plant species richness is a primary indicator of biodiversity. This paper uses spectral variance to predict vegetation richness, known as Spectral Variation Hypothesis. Hierarchical agglomerative clustering is our primary tool to retrieve clusters whose Shannon entropy should refle...

Many researchers have studied the population of individuals with mild mental retardation (MIMR) as if it is a clear entity. Few researchers have investigated potential subtypes within the MIMR population. The purpose of the present study was to investigate which subtypes can be identified on the basis of intellectual, adaptive and behavioral functioning. Seventy-three individuals with MIMR were...