A EM Probabilistic Clustering Algorithm for Large Scale Data Sets based on Partial Constraints Information

2011 ◽  
Vol 3 (10) ◽  
pp. 20-29
Author(s):  
Shen Yan ◽  
Song Shunlin ◽  
Zhu Yuquan
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Data Sets ◽  
Probabilistic Clustering ◽  
Large Scale Data ◽  
Scale Data ◽  
Large Scale Data Sets

scholarly journals Privacy-preserving constrained spectral clustering algorithm for large-scale data sets

2020 ◽  
Vol 14 (3) ◽  
pp. 321-331 ◽  
Author(s):  
Ji Li ◽  
Jianghong Wei ◽  
Mao Ye ◽  
Wenfen Liu ◽  
Xuexian Hu
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Data Sets ◽  
Large Scale Data ◽  
Spectral Clustering Algorithm ◽  
Scale Data ◽  
Large Scale Data Sets

Landmark FN-DBSCAN: An Efficient Density-Based Clustering Algorithm with Fuzzy Neighborhood

2013 ◽  
Vol 17 (1) ◽  
pp. 60-73
Author(s):  
Hao Liu ◽  
◽  
Satoshi Oyama ◽  
Masahito Kurihara ◽  
Haruhiko Sato
Keyword(s):  
Time Complexity ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Set ◽  
Large Scale Data ◽  
Density Based Clustering ◽  
Scale Data ◽  
Large Scale Data Sets

Clustering is an important tool for data analysis and many clustering techniques have been proposed over the past years. Among them are density-based clustering methods, which have several benefits such as the number of clusters is not required before carrying out clustering; the detected clusters can be represented in an arbitrary shape and outliers can be detected and removed. Recently, the density-based algorithms were extended with the fuzzy set theory, which has made these algorithm more robust. However, the density-based clustering algorithms usually require a time complexity ofO(n2) wherenis the number of data in the data set, implying that they are not suitable to work with large scale data sets. In this paper, a novel clustering algorithm called landmark fuzzy neighborhood DBSCAN (landmark FN-DBSCAN) is proposed. The concept, landmark, is used to represent a subset of the input data set which makes the algorithm efficient on large scale data sets. We give a theoretical analysis on time complexity and space complexity, which shows both of them are linear to the size of the data set. The experiments show that the landmark FN-DBSCAN is much faster than FN-DBSCAN and provides a very good quality of clustering.

Artificial Neural Network Models for Large-Scale Data

2019 ◽  
pp. 406-439
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran
Keyword(s):  
Large Scale ◽  
Network Models ◽  
Data Sets ◽  
Neural Network Models ◽  
Large Scale Data ◽  
The World ◽  
Commercial Applications ◽  
Artificial Neural Network Models ◽  
Scale Data ◽  
Large Scale Data Sets

Artificial intelligence (ARTINT) and information have been famous fields for many years. A reason has been that many different areas have been promoted quickly based on the ARTINT and information, and they have created many significant values for many years. These crucial values have certainly been used more and more for many economies of the countries in the world, other sciences, companies, organizations, etc. Many massive corporations, big organizations, etc. have been established rapidly because these economies have been developed in the strongest way. Unsurprisingly, lots of information and large-scale data sets have been created clearly from these corporations, organizations, etc. This has been the major challenges for many commercial applications, studies, etc. to process and store them successfully. To handle this problem, many algorithms have been proposed for processing these big data sets.

Collection, Storage, Protection, and Sharing Issues With Large-Scale Data Sets

2017 ◽  
Author(s):  
Shirley M. Matteson ◽  
Sonya E. Sherrod ◽  
Sevket Ceyhun Cetin
Keyword(s):  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Scale Data ◽  
Large Scale Data Sets
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