the species of dioscorea (yam) are regarded as a staple food crop for millions of people in the tropical and subtropical regions of the world. it is regarded as an important food crop next to cereals and grains due to high yield storage of carbohydrates. economically, only few species are recognized for cultivation from agricultural point of view, in spite of its large species diversity. the sp...

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...

The hierarchy is often used to infer knowledge from groups of items and relations in varying granularities. Hierarchical clustering algorithms take an input of pairwise data-item similarities and output a hierarchy of the data-items. This paper presents Bidirectional agglomerative hierarchical clustering to create a hierarchy bottom-up, by iteratively merging the closest pair of data-items into...

A hierarchical agglomerative clustering algorithm based on the analysis of dissimilarity increments between neighboring patterns is presented. The first derivative of dissimilarity between neighboring patterns inside a natural cluster is modelled by an exponential distribution, this statistic characterizing the cluster. A cluster isolation criterion is defined based on estimates of each cluster...

We explore the use of instance and cluster-level constraints with agglomerative hierarchical clustering. Though previous work has illustrated the benefits of using constraints for non-hierarchical clustering, their application to hierarchical clustering is not straight-forward for two primary reasons. First, some constraint combinations make the feasibility problem (Does there exist a single fe...

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...

This paper describes the findings from evaluating the performance of agglomerative hierarchical cluster methods for determining seasonal factor groups. Seasonal factor groups are usually determined by traditional cluster analysis based on various similarity measures. Agglomerative hierarchical methods merge telemetry traffic monitoring sites (TTMSs) into groups according to their similarities. ...

Research in cluster analysis has resulted in a large number of algorithms and similarity measurements for clustering scienti c data. Machine learning researchers have published a number of methods for conceptual clustering, in which observations are grouped into clusters which have \good" descriptions in some language. We investigate the general properties which similarity metrics, objective fu...

This paper presents a new approach to agglomerative hierarchical clustering. Classical hierarchical clustering algorithms are based on metrics which only consider the absolute distance between two clusters, merging the pair of clusters with highest absolute similarity. We propose a relative dissimilarity measure, which considers not only the distance between a pair of clusters, but also how dis...