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 ...

We develop an algorithm that is fast and scalable in the detection of a nested partition extracted from dendrogram obtained hierarchical clustering multivariate series. Our provides p-value for each clade observed tree. The by computing many bootstrap replicas dissimilarity matrix performing statistical test on difference between associated with given its parent node. prove efficacy our set ben...

Hierarchical load forecasting (HLF) is an approach to generate forecasts for hierarchical time series. The performance of HLF can be improved by optimizing the model and structure. Previous studies mostly focus on while structure usually formed clustering natural attributes like geography, customer type, or similarities between profiles. A major limitation these structures mismatched objectives...

This paper introduces a hybrid hierarchical clustering method, which is a novel method for speeding up agglomerative hierarchical clustering by seeding the algorithm with clusters obtained from K-means clustering. This work describes a benchmark study comparing the performance of hybrid hierarchical clustering to that of conventional hierarchical clustering. The two clustering methods are compa...

Kebakaran hutan di Indonesia setiap tahunnya masih sering terjadi. Dalam menanggulangi kebakaran sendiri, para peneliti belakangan ini semakin fokus untuk melakukan pengembangan sistem yang mampu prediksi hutan. Selain mengenai hutan, antisipasi dapat dilakukan membantu menangani pencegahan salah satunya adalah pengelompokkan terhadap wilayah memiliki potensi kebakaran. Pada penelitian Clusteri...

In this paper we present an extended version of Evolving Trees using Oja’s rule. Evolving Trees are extensions of Self-Organizing Maps developed for hierarchical classification systems. Therefore they are well suited for taxonomic problems like the identification of bacteria. The paper focus on clustering and visualization of bacteria measurements. A modified variant of the Evolving Tree is dev...