scholarly journals Clustering Life Trajectories a New Divisive Hierarchical Clustering Algorithm for Discrete-Valued Discrete Time Series

2011 ◽  
Author(s):  
Stephan Dlugosz
Keyword(s):  
Time Series ◽  
Hierarchical Clustering ◽  
Discrete Time ◽  
Clustering Algorithm ◽  
Discrete Time Series ◽  
Life Trajectories ◽  
Divisive Hierarchical Clustering ◽  
Hierarchical Clustering Algorithm

Finding specific peaks (markers) using fuzzy divisive hierarchical associative-clustering based on the chromatographic profiles of medicinal plant extracts obtained at various detection wavelengths

2020 ◽  
Vol 12 (25) ◽  
pp. 3260-3267
Author(s):  
Ileana M. Simion ◽  
Augustin-C. Moţ ◽  
Costel Sârbu
Keyword(s):  
Medicinal Plant ◽  
Hierarchical Clustering ◽  
Plant Extracts ◽  
Clustering Algorithm ◽  
Chemometric Methods ◽  
Fuzzy C Means ◽  
Divisive Hierarchical Clustering ◽  
Medicinal Plant Extracts ◽  
Hierarchical Clustering Algorithm ◽  
Chromatographic Profiles

Advanced chemometric methods, such as fuzzy c-means (FCM), a fuzzy divisive hierarchical clustering algorithm (FDHC), and fuzzy divisive hierarchical associative-clustering (FDHAC), have been successfully applied in this study.

scholarly journals Rebalancing Strategy for Bike-Sharing Systems Based on the Model of Level of Detail

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhenghua Hu ◽  
Kejie Huang ◽  
Enyou Zhang ◽  
Qi’ang Ge ◽  
Xiaoxue Yang
Keyword(s):  
Genetic Algorithm ◽  
Hierarchical Clustering ◽  
Traditional Method ◽  
Clustering Algorithm ◽  
Level Of Detail ◽  
Daily Lives ◽  
Effective Time ◽  
Divisive Hierarchical Clustering ◽  
Bike Sharing ◽  
Hierarchical Clustering Algorithm

Traveling by bike-sharing systems has become an indispensable means of transportation in our daily lives because green commuting has gradually become a consensus and conscious action. However, the problem of “difficult to rent or to return a bike” has gradually become an issue in operating the bike-sharing system. Moreover, scientific and systematic schemes that can efficiently complete the task of rebalancing bike-sharing systems are lacking. This study aims to introduce the basic idea of the k-divisive hierarchical clustering algorithm. A rebalancing strategy based on the model of level of detail in combination with genetic algorithm was proposed. Data were collected from the bike-sharing system in Ningbo. Results showed that the proposed algorithm could alleviate the problem of the uneven distribution of the demand for renting or returning bikes and effectively improve the service from the bike-sharing system. Compared with the traditional method, this algorithm helps reduce the effective time for rebalancing bike-sharing systems by 28.3%. Therefore, it is an effective rebalancing scheme.

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%

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