Data Summarization Based Fast Hierarchical Clustering Method for Large Datasets

2009 ◽  
Author(s):  
Bidyut Kr. Patra ◽  
Sukumar Nandi ◽  
P. Viswanath
Keyword(s):  
Hierarchical Clustering ◽  
Large Datasets ◽  
Clustering Method ◽  
Data Summarization

scholarly journals A hierarchical clustering method for random intervals based on a similarity measure

2021 ◽  
Author(s):  
Ana Belén Ramos-Guajardo
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Real Life ◽  
Stopping Criterion ◽  
Clustering Method ◽  
Bootstrap Test ◽  
Empirical Performance ◽  
Random Intervals ◽  
Expected Values

AbstractA new clustering method for random intervals that are measured in the same units over the same group of individuals is provided. It takes into account the similarity degree between the expected values of the random intervals that can be analyzed by means of a two-sample similarity bootstrap test. Thus, the expectations of each pair of random intervals are compared through that test and a p-value matrix is finally obtained. The suggested clustering algorithm considers such a matrix where each p-value can be seen at the same time as a kind of similarity between the random intervals. The algorithm is iterative and includes an objective stopping criterion that leads to statistically similar clusters that are different from each other. Some simulations to show the empirical performance of the proposal are developed and the approach is applied to two real-life situations.

scholarly journals Statistical Analysis of Word Frequency Distribution in Lithuanian Texts of Different Genres

2016 ◽  
Vol 55 (1) ◽  
pp. 61-69
Author(s):  
Neringa Bružaitė ◽  
Tomas Rekašius
Keyword(s):  
Statistical Analysis ◽  
Word Frequency ◽  
Frequency Distribution ◽  
Hierarchical Clustering ◽  
Distance Measure ◽  
Structural Type ◽  
Clustering Method ◽  
Jaccard Distance ◽  
Word Frequencies ◽  
Word Frequency Distribution

The paper examines Lithuanian texts of different authors and genres. The main points ofinterest – the number of words, the number of different words and word frequencies. Structural type distributionand Zipf’s law are applied for describing the frequency distribution of words in the text. It is obvious that thelexical diversity of any text can be defined by different words that are used in the text, also called vocabulary.It is shown that the information contained in a reduced vocabulary is enough for dividing the texts analyzedin this article into groups by genre and author using a hierarchical clustering method. In this case, distancesbetween clusters are measured using the Jaccard distance measure, and clusters are aggregated using the Wardmethod.

A hierarchical clustering method for analyzing functional MR images

1999 ◽  
Vol 17 (6) ◽  
pp. 817-826 ◽  
Author(s):  
Peter Filzmoser ◽  
Richard Baumgartner ◽  
Ewald Moser
Keyword(s):  
Hierarchical Clustering ◽  
Mr Images ◽  
Clustering Method
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