scholarly journals Data clustering analysis of early reflections in small room

2018 ◽  
Vol 144 (4) ◽  
pp. EL328-EL332 ◽  
Author(s):  
Zhaoqi Zhang ◽  
Ge Zhu ◽  
Yong Shen
Keyword(s):  
Clustering Analysis ◽  
Data Clustering ◽  
Small Room

scholarly journals Semiparametric Estimation and Panel Data Clustering Analysis Based on D-Vine and C-Vine

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Hong Li ◽  
Yuantao Xie ◽  
Juan Yang ◽  
Di Wang
Keyword(s):  
Panel Data ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Semiparametric Estimation ◽  
Similarity Measurement ◽  
Model Estimation ◽  
Similarity Coefficients ◽  
Clustering Model ◽  
Compound Model

This paper proposed a panel data clustering model based on D-vine and C-vine and supported a semiparametric estimation for parameters. These models include a two-step inference function for margins, two-step semiparameter estimation, and stepwise semiparametric estimation. In similarity measurement, similarity coefficients are constructed by a multivariate Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models, which are HNAC and D-vine compound model and HNAC and C-vine compound model. Estimation solutions and models evaluation are given for these models. In the case study, the clustering results of HNAC and D-vine compound model and HNAC and C-vine compound model are given, and the effect of different copula families on clustering results is also discussed. The result shows the models are effective and useful.

scholarly journals Application of Bio-inspired Metaheuristics in the Data Clustering Problem

2011 ◽  
Vol 14 (3) ◽  
Author(s):  
Thelma Elita Colanzi ◽  
Wesley Klewerton Guez Assunção ◽  
Aurora Trinidad Ramirez Pozo ◽  
Ana Cristina B. Kochem Vendramin ◽  
Diogo Augusto Barros Pereira ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Considerable Effort ◽  
Number Of Clusters ◽  
Immune Systems ◽  
Clustering Problem ◽  
Natural Groups

Clustering analysis includes a number of different algorithms and methods for grouping objects by their similar characteristics into categories. In recent years, considerable effort has been made to improve such algorithms performance. In this sense, this paper explores three different bio-inspired metaheuristics in the clustering problem: Genetic Algorithms (GAs), Ant Colony Optimization (ACO), and Artificial Immune Systems (AIS). This paper proposes some refinements to be applied to these metaheuristics in order to improve their performance in the data clustering problem. The performance of the proposed algorithms is compared on five different numeric UCI databases. The results show that GA, ACO and AIS based algorithms are able to efficiently and automatically forming natural groups from a pre-defined number of clusters.

scholarly journals Data Clustering Analysis on Grassmann Manifold Metric

2017 ◽  
Vol 10 (1) ◽  
pp. 213-224
Author(s):  
Yinghong Xie ◽  
Yuqing He ◽  
Xiaosheng Yu ◽  
Xindong You ◽  
Qiang Guo
Keyword(s):  
Clustering Analysis ◽  
Data Clustering ◽  
Grassmann Manifold

Clustering Techniques and Their Applications: A Review

2020 ◽  
Vol 1 (4) ◽  
pp. 1-6
Author(s):  
Arjun Dutta
Keyword(s):  
Data Mining ◽  
Image Segmentation ◽  
Unsupervised Learning ◽  
Clustering Analysis ◽  
Data Clustering ◽  
Clustering Method ◽  
Clustering Techniques ◽  
Partitional Clustering ◽  
Gene Information ◽  
Scientific Fields

This paper deals with concise study on clustering: existing methods and developments made at various times. Clustering is defined as an unsupervised learning where the targets are sorted out on the foundation of some similarity inherent among them. In the recent times, we dispense with large masses of data including images, video, social text, DNA, gene information, etc. Data clustering analysis has come out as an efficient technique to accurately achieve the task of categorizing information into sensible groups. Clustering has a deep association with researches in several scientific fields. k-means algorithm was suggested in 1957. K-mean is the most popular partitional clustering method till date. In many commercial and non-commercial fields, clustering techniques are used. The applications of clustering in some areas like image segmentation, object and role recognition and data mining are highlighted. In this paper, we have presented a brief description of the surviving types of clustering approaches followed by a survey of the areas.

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