A Comparative Study of Divisive and Agglomerative Hierarchical Clustering Algorithms
نویسندگان
چکیده
منابع مشابه
Divisive Hierarchical Clustering with K-means and Agglomerative Hierarchical Clustering
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...
متن کاملA comparative study of divisive hierarchical clustering algorithms
A general scheme for divisive hierarchical clustering algorithms is proposed. It is made of three main steps : first a splitting procedure for the subdivision of clusters into two subclusters, second a local evaluation of the bipartitions resulting from the tentative splits and, third, a formula for determining the nodes levels of the resulting dendrogram. A handfull of such algorithms is given...
متن کاملModern hierarchical, agglomerative clustering algorithms
This paper presents algorithms for hierarchical, agglomerative clustering which perform most efficiently in the general-purpose setup that is given in modern standard software. Requirements are: (1) the input data is given by pairwise dissimilarities between data points, but extensions to vector data are also discussed (2) the output is a “stepwise dendrogram”, a data structure which is shared ...
متن کامل2 Review of Agglomerative Hierarchical Clustering Algorithms
Hierarchical methods are well known clustering technique that can be potentially very useful for various data mining tasks. A hierarchical clustering scheme produces a sequence of clusterings in which each clustering is nested into the next clustering in the sequence. Since hierarchical clustering is a greedy search algorithm based on a local search, the merging decision made early in the agglo...
متن کاملFuzzification of Agglomerative Hierarchical Crisp Clustering Algorithms
User generated content from fora, weblogs and other social networks is a very fast growing data source in which different information extraction algorithms can provide a convenient data access. Hierarchical clustering algorithms are used to provide topics covered in this data on different levels of abstraction. During the last years, there has been some research using hierarchical fuzzy algorit...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Classification
سال: 2018
ISSN: 0176-4268,1432-1343
DOI: 10.1007/s00357-018-9259-9