An important problem in the application of cluster analysis is the decision regarding how many clusters should be derived from the data. The aim of the paper is to determine a number of clusters with a distinctive breaking point (elbow), calculating variance ratio criterion (VRC) by Calinski and Harabasz and J-index in order to check robustness of cluster solutions. Agglomerative hierarchical c...

− We present the applicability of hierarchical agglomerative cluster algorithms to terahertz (THz) spectroscopic analysis. We show the influence of different windowing and filtering methods in the spectral data preprocessing to enhance the clustering results. Two distance measures are compared. Classical Euclidean distance on the full frequency range and a distance working only on the minima of...

Clustering is one of the most interesting and important tool for research in data mining and other disciplines. The aim of clustering is to find the relationship among the data objects, and classify them into meaningful subgroups. The effectiveness of clustering algorithms depends on the appropriateness of the similarity measure between the data in which the similarity can be computed. This pap...

One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across many different fields ranging from computational biology to social sciences to computer vision in part because their output is easy to interpret. Unfortunately, it is well known, however, that many of the classic agglomerative clustering algorithms are not robust to...

Adaptive tree structured clustering (ATSC) is our proposed divisive hierarchical clustering method that recursively divides a data set into 2 subsets using self-organizing feature map (SOM). In each partition, the data set is quantized by SOM and the quantized data is divided using agglomerative hierarchical clustering. ATSC can divide data sets regardless of data size in feasible time. On the ...

Clustering groups records that are similar to each other into the same group, and those that are less similar into different groups. Clustering data of mixed types is difficult due to different data characteristics. Extending Gower’s metric for nominal and ordinal data is incorporated into an agglomerative hierarchical clustering algorithm to cluster mixed type data. This paper describes the ex...

Partial orders and directed acyclic graphs are commonly recurring data structures that arise naturally in numerous domains applications used to represent ordered relations between entities the domains. Examples task dependencies a project plan, transaction order distributed ledgers execution sequences of tasks computer programs, just mention few. We study problem preserving hierarchical cluster...

A new unsupervised classification algorithm is introduced for fully polarimetric SAR data. The agglomerative hierarchical algorithm and Wishart test statistics are used for the cluster segmentation, which includes the process of estimation the number of clusters. The Cloude-Pottier decomposition & HSI color transform are used for the target identification, which also automatically render the co...