Privacy-preserving constrained spectral clustering algorithm for 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.

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An improved affinity propagation clustering algorithm for large-scale data sets

2013 Ninth International Conference on Natural Computation (ICNC) ◽

10.1109/icnc.2013.6818103 ◽

2013 ◽

Cited By ~ 2

Author(s):

Xiaonan Liu ◽

Meijuan Yin ◽

Junyong Luo ◽

Wuping Chen

Keyword(s):

Large Scale ◽

Clustering Algorithm ◽

Affinity Propagation ◽

Data Sets ◽

Large Scale Data ◽

Affinity Propagation Clustering ◽

Scale Data ◽

Large Scale Data Sets

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Faculty Opinions recommendation of Comparative assessment of large-scale data sets of protein-protein interactions.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1006598.82257 ◽

2002 ◽

Author(s):

Rob Russell

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Comparative Assessment ◽

Data Sets ◽

Protein Protein Interactions ◽

Large Scale Data ◽

Scale Data ◽

Large Scale Data Sets

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Pattern Recognition in Large-Scale Data Sets: Application in Integrated Circuit Manufacturing

Big Data Analytics - Lecture Notes in Computer Science ◽

10.1007/978-3-319-03689-2_13 ◽

2013 ◽

pp. 185-196 ◽

Cited By ~ 1

Author(s):

Choudur K. Lakshminarayan ◽

Michael I. Baron

Keyword(s):

Pattern Recognition ◽

Integrated Circuit ◽

Large Scale ◽

Data Sets ◽

Integrated Circuit Manufacturing ◽

Large Scale Data ◽

Scale Data ◽

Large Scale Data Sets

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WebViz: A Web-Based Collaborative Interactive Visualization System for Large-Scale Data Sets

Lecture Notes in Earth System Sciences - GPU Solutions to Multi-scale Problems in Science and Engineering ◽

10.1007/978-3-642-16405-7_37 ◽

2013 ◽

pp. 587-606 ◽

Cited By ~ 2

Author(s):

Yichen Zhou ◽

Robin M. Weiss ◽

Elizabeth McArthur ◽

David Sanchez ◽

Xiang Yao ◽

...

Keyword(s):

Large Scale ◽

Interactive Visualization ◽

Data Sets ◽

Web Based ◽

Visualization System ◽

Large Scale Data ◽

Scale Data ◽

Large Scale Data Sets

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Artificial Neural Network Models for Large-Scale Data

Advances in Data Mining and Database Management - Handbook of Research on Big Data and the IoT ◽

10.4018/978-1-5225-7432-3.ch022 ◽

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.

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