Trust-Similarity Measure-Based Hierarchical Clustering Method

2022 ◽  
pp. 49-69
Author(s):  
Su-Min Yu ◽  
Zhi-Jiao Du
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Method

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.

A Power Network Partitioning Method Based on Local Similarity Measure

2012 ◽  
Vol 588-589 ◽  
pp. 364-367
Author(s):  
Tao Wang ◽  
Heng Zhou ◽  
Pan Zou
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Reactive Power ◽  
Graph Model ◽  
Weighted Graph ◽  
Local Similarity ◽  
Network Partitioning ◽  
Power Network ◽  
Clustering Method ◽  
Partitioning Algorithm

A power network partitioning model based on the weighed local similarity measure is presented in this paper considering the regional decoupling characteristics of reactive power. A weighted graph model of reactive power network is established and a new measurement of local similarity based on weighed graph is defined. To utilize our measurement of similarity to partition reactive power network, a partitioning algorithm based on generalized ward hierarchical clustering method is proposed. The algorithm can ensure balance of the reactive power inside partition. Applying the proposed algorithm to IEEE 39-bus system, the results show that the proposed algorithm is feasible and effective.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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.

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