In Hierarchical Clustering, a set of patterns are partitioned into a sequence of groups represented as a dendrogram. The dendrogram is a tree representation where each node is associated with merging of two (or more) partitions and hence each partition is nested into the next partition. Hierarchical representation has properties that are useful for visualization and interpretation of clustering...

The similarity measure based nearest neighbour classifier is commonly used in object recognition or retrieval systems. The result of a query in such a system is in general the images from the database whose descriptors are closest to the descriptor of the query image. An important issue in large scale recognition systems is the computational burden caused by measuring the similarity of the feat...

The present study was conducted to assess the rapid molecular identification and characterization of 45 Vibrio parahaemolyticus isolates from 15 samples of 3 different types of fish (Kembung, Bawal and Sangeh) in the Kuching-Samarahan district. Polymerase chain reaction (PCR) based confirmation was done targeting the 450 bp fragment of the thermolabile (tl) gene, while DNA fingerprinting was pe...

Clustering is the task of grouping similar objects into clusters. A prominent and useful class of algorithm is hierarchical agglomerative clustering (HAC) which iteratively agglomerates the closest pair until all data points belong to one cluster. It outputs a dendrogram showing all N levels of agglomerations where N is the number of objects in the dataset. However, HAC methods have several dra...

Dendrograms and minimum-weight spanning trees (MWST) are discrete structures which arise in clustering theory, networks, and where strategic choices are made between discrete options. The discrete structure of the solution tree varies discontinuously with respect to changes in the dissimilarity or cost matrix. Tarjan [1982] has obtained bounds on the changes to the elements of the cost matrix, ...

Clustering ensembles has been recently recognized as an emerging approach to provide more robust solutions to the data clustering problem. Current methods of clustering ensembles typically fall into instance-based, cluster-based, or hybrid approaches; however, most of such methods fail in discriminating among the various clusterings that participate to the ensemble. In this paper, we address th...

2 Hierarchic Clustering 1 2.1 Description of Classes . . . . . . . . . . . . . . . . . . . . . . . . 4 2.2 Numbers of Classes . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.3 Clustering and Ordination . . . . . . . . . . . . . . . . . . . . . . 5 2.4 Reordering a Dendrogram . . . . . . . . . . . . . . . . . . . . . . 6 2.5 Minimum Spanning Tree . . . . . . . . . . . . . . . . . . . . ....

While there is a very long tradition of approximating a data array by projecting row or column vectors into a lower dimensional subspace the direct approximation of a data matrix through smoothing is less common. Applications of data array smoothing include visualization; filtering of less relevant, and thus harder to interpret, values; and as a means towards compression. Wavelet smoothing or r...

Asymmetric cluster analysis is one of the most useful methods together with asymmetric multidimensional scaling (MDS) to analyze asymmetric (dis)similarity data. In both methods, visualization of the result of the analysis plays an important role in the analysis. Some methods for visualizing the result of the asymmetric clustering and MDS have been proposed (Saito and Yadohisa, Data Analysis of...

Provinsi Sulawesi Selatan merupakan salah satu provinsi di urutan teratas yang angka positif Covid-19 tinggi. Juga, penambahan kasus harian dan jumlah kematian pasien akibat relatif Mencermati keadaan ini, diperlukan kajian dapat memetakan resiko penanggulangan dari segi statistik pandemi (total kumulatif, sembuh, meninggal, dirawat) dikombinasi dengan karakteristik ekonomi, kesehatan, sosial, ...