Business Data Analysis Based on Hierarchical Clustering Algorithm in the Context of Big Data

The clustering analysis techniques play an important role in the area of data mining. Although from existence several clustering techniques. However, it still to their tries to improve the clustering process efficiently or propose new techniques seeks to allocate objects into clusters so that two objects in the same cluster are more similar than two objects in different clusters and careful not to duplicate the same objects in different groups with the ability to cover all data as much as possible. This paper presents two directions. The first is to propose a new algorithm that coined a name (MB Algorithm) to collect unlabeled data and put them into appropriate groups. The second is the creation of a lexical sequence sentence (LCS) based on similar semantic sentences which are different from the traditional lexical word chain (LCW) based on words. The results showed that the performance of the MB algorithm has generally outperformed the two algorithms the hierarchical clustering algorithm and the K-mean algorithm.

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Handling WSD using Hierarchical Clustering Algorithm with sentences

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset1841120 ◽

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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Based on a hierarchical clustering algorithm: detecting the community structure of a resting state brain network

Future Computer and Information Technology ◽

10.2495/icfcit130781 ◽

2013 ◽

Author(s):

Wenzhao Liu ◽

Limin Niu ◽

Junjie Chen

Keyword(s):

Community Structure ◽

Hierarchical Clustering ◽

Resting State ◽

Clustering Algorithm ◽

Brain Network ◽

Hierarchical Clustering Algorithm

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Evaluation of students programming skills on a computer programming course with a hierarchical clustering algorithm

2020 IEEE Frontiers in Education Conference (FIE) ◽

10.1109/fie44824.2020.9274130 ◽

2020 ◽

Author(s):

Davi Bernardo Silva ◽

Carlos N. Silla

Keyword(s):

Hierarchical Clustering ◽

Clustering Algorithm ◽

Computer Programming ◽

Programming Skills ◽

Hierarchical Clustering Algorithm

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Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

Mathematics ◽

10.3390/math9040370 ◽

2021 ◽

Vol 9 (4) ◽

pp. 370

Author(s):

Shuangsheng Wu ◽

Jie Lin ◽

Zhenyu Zhang ◽

Yushu Yang

Keyword(s):

Hierarchical Clustering ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Agglomerative Hierarchical Clustering ◽

Research Gaps ◽

Judicial Practice ◽

Linguistic Term ◽

Clustering Effect ◽

Hierarchical Clustering Algorithm ◽

Fuzzy Linguistic

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information.

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