Abstract In hierarchical cluster analysis dendrogram graphs are used to visualize how clusters are formed. I propose an alternative graph named “clustergram” to examine how cluster members are assigned to clusters as the number of clusters increases. This graph is useful in exploratory analysis for non-hierarchical clustering algorithms like k-means and for hierarchical cluster algorithms when ...

The hierarchical clustering is an efficient way to reduce the overall energy consumption within the cluster by performing aggregation and fusion of data. Hence, the amount of transmitting information to the base station is decreased. Clusters create hierarchical Wireless Sensor Networks (WSNs) which facilitate efficient utilization of limited resources of sensor nodes and thus extend network li...

This work analyzes the distribution of past CS229 projects by applying hierarchical agglomerative clustering. The clusters reveal which topics are very popular and which topics are more unique. Tracking the clusters over time also provides insight into how student projects have shifted over time. This knowledge will help future students select interesting and unique projects.

We describe the Hierarchical Classifier (HC), which is a hybrid architecture [1] built with the help of supervised training and unsu-pervised problem clustering. We prove a theorem giving the estimationˆR of HC risk. The proof works because of an improved way of computing cluster weights, introduced in this paper. Experiments show thatˆR is correlated with HC real error. This allows us to usê R...

In wide area backup protection of electric power systems, the prerequisite of protection device’s accurate, fast and reliable performance is its corresponding fault type and fault location can be discriminated quickly and defined exactly. In our study, global information will be introduced into the backup protection system. By analyzing and computing real-time PMU measurements, basing on cluste...

To implement divisive hierarchical clustering algorithm with K-means and to apply Agglomerative Hierarchical Clustering on the resultant data in data mining where efficient and accurate result. In Hierarchical Clustering by finding the initial k centroids in a fixed manner instead of randomly choosing them. In which k centroids are chosen by dividing the one dimensional data of a particular clu...

Clustering has been widely used to partition data into groups so that the degree of association is high among members of the same group and low among members of diierent groups. Though many eeective and eecient clustering algorithms have been developed and deployed, most of them still suuer from the lack of automatic or online decision for optimal number of clusters. In this paper, we deene clu...

the main objective of this study was to investigate how months cluster in any thermal regions based on temperature. for this purpose, the mean of daily temperature data have been provided using 620 synoptic and climatology stations. then, mean temperature was converted for any station, based on solar calendar, and maps of mean daily temperature have been interpolated using kriging method. spati...