Multiple factor hierarchical clustering algorithm for large scale web page and search engine clickstream data

2010 ◽  
Vol 197 (1) ◽  
pp. 123-134 ◽  
Author(s):  
Gang Kou ◽  
Chunwei Lou
Keyword(s):  
Search Engine ◽  
Hierarchical Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Web Page ◽  
Clickstream Data ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Factor

scholarly journals Partial Context Similarity of Gene/Proteins in Leukemia Using Context Rank Based Hierarchical Clustering Algorithm

2015 ◽  
Vol 5 (3) ◽  
pp. 483
Author(s):  
Shahana Bano ◽  
K.Rajasekara Rao
Keyword(s):  
Natural Language Processing ◽  
Hierarchical Clustering ◽  
Language Processing ◽  
Large Scale ◽  
Protein Identification ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Filtering Algorithm ◽  
Context Similarity ◽  
Hierarchical Clustering Algorithm

In this paper we proposed a method which avoids the choice of natural language processing tools such as pos taggers and parsers reduce the processing overhead. Moreover, we suggest a structure to immediately create a large-scale corpus annotated along with disease names, which can be applied to train our probabilistic model. In this proposed work context rank based hierarchical clustering method is applied on different datasets namely colon, Leukemia, MLL medical diseases. Optimal rule filtering algorithm is applied on these datasets to remove unwanted special characters for gene/protein identification. Finally, experimental results show that proposed method outperformed existing methods in terms of time and clusters space.

A New Method for Hierarchical Clustering Analysis of Large-Scale Machine Tools

2014 ◽  
Author(s):  
Xianming Gao ◽  
Jun Hong ◽  
Shuai Zheng ◽  
Yichao Zhen
Keyword(s):  
Hierarchical Clustering ◽  
Machine Tools ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Structure Mapping ◽  
Module Design ◽  
Weight Design ◽  
Design Requirements ◽  
Hierarchical Clustering Algorithm ◽  
Module Partition

Due to the various requirements of users and the complexity of machine tools, most machine tools were produced in a small-batch production mode, resulting in the largely prolonged design cycle and the reduced competitive ability of enterprise. To improve the design efficiency and reply to the changeful design requirements, the module design has been expected to be used in the rapid design of machine tools. To achieve the module design of machine tools, this paper proposed a method of module partition (one important step of module design) based on the function/means trees method and hierarchical clustering algorithm. The initial unit is firstly obtained by function/means trees method, and then the degree of correlation and its weight between function units is analyzed and calculated. Subsequently, a hierarchical module partition result (a hierarchical diagram) is calculated by hierarchical clustering algorithm. Furthermore, the function-structure mapping of the NC machine was achieved using an axiomatic design method focusing on the design requirement with one order of weight. In addition, this paper taken an example (precision horizontal machining center THM65160) to validate and analyze the characteristics of this method of module partition, and the test and analysis results showed that the size of initial function unit matrix is efficiently reduced by using function /means trees method, and the module result (which calculated by the hierarchical clustering algorithm) was helpful to achieve a rapid retrieval of module partition results for the products with the multi-weight design requirements. Compared to the other clustering algorithms, it can be summarized that this proposed method was beneficial to achieve the rapid clustering calculations and the relevant module design of products (which had the multi-weight design requirements).

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

Mathematics ◽  
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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