GENERALIZED PARTICLE MODEL USED FOR DATA CLUSTERING

2006 ◽  
Vol 20 (07) ◽  
pp. 1001-1028 ◽  
Author(s):  
DIANXUN SHUAI ◽  
XUE FANGLIANG
Keyword(s):  
Data Clustering ◽  
Large Scale ◽  
Hardware Implementation ◽  
Learning Ability ◽  
High Dimensional ◽  
Particle Model ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Clustering Approach

Data clustering has been widely used in many areas, such as data mining, statistics, machine learning and so on. A variety of clustering approaches have been proposed so far, but most of them are not qualified to quickly cluster a large-scale high-dimensional database. This paper is devoted to a novel data clustering approach based on a generalized particle model (GPM). The GPM transforms the data clustering process into a stochastic process over the configuration space on a GPM array. The proposed approach is characterized by the self-organizing clustering and many advantages in terms of the insensitivity to noise, quality robustness to clustered data, suitability for high-dimensional and massive data sets, learning ability, openness and easier hardware implementation with the VLSI systolic technology. The analysis and simulations have shown the effectiveness and good performance of the proposed GPM approach to data clustering.

Neural Network for Big Data Sets

2022 ◽  
pp. 41-67
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Computer Science ◽  
Large Scale ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Large Scale Data ◽  
Commercial Applications ◽  
Novel Model

Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).

scholarly journals Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets

2016 ◽  
Vol 38 (4) ◽  
pp. B521-B538 ◽  
Author(s):  
Paris Perdikaris ◽  
Daniele Venturi ◽  
George Em Karniadakis
Keyword(s):  
Information Fusion ◽  
High Dimensional ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets

Neural Network for Big Data Sets

2019 ◽  
pp. 271-303
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Computer Science ◽  
Large Scale ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Large Scale Data ◽  
Commercial Applications ◽  
Novel Model

Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).

scholarly journals The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch

2010 ◽  
Vol 2010 ◽  
pp. 1-16 ◽  
Author(s):  
Meyer Z. Pesenson ◽  
Isaac Z. Pesenson ◽  
Bruce McCollum
Keyword(s):  
Applied Mathematics ◽  
Big Bang ◽  
The Other ◽  
High Dimensional ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Astronomical Data ◽  
Illustrative Visualization ◽  
The One

Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely because of the size in bytes of the data sets, but also because of the complexity of modern data sets. Mathematical limitations of familiar algorithms and techniques in dealing with such data sets create a critical need fornew paradigmsfor the representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multiresolution data across application domains. Some of the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based geosciences. Unfortunately, valuable results pertaining to these problems are mostly to be found in publications outside of astronomy. Here we offer brief overviews of a number of concepts, techniques and developments that are vital to the analysis and visualization of complex datasets and images. One of the goals of this paper is to help bridge the gap between applied mathematics and artificial intelligence on the one side and astronomy on the other.

scholarly journals Fundamental resource trade-offs for encoded distributed optimization

2020 ◽  
Author(s):  
A Salman Avestimehr ◽  
Seyed Mohammadreza Mousavi Kalan ◽  
Mahdi Soltanolkotabi
Keyword(s):  
Computational Time ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Computational Framework ◽  
Data Set ◽  
Trade Offs ◽  
Major Bottleneck ◽  
Computing Environments ◽  
Analyze Data

Abstract Dealing with the shear size and complexity of today’s massive data sets requires computational platforms that can analyze data in a parallelized and distributed fashion. A major bottleneck that arises in such modern distributed computing environments is that some of the worker nodes may run slow. These nodes a.k.a. stragglers can significantly slow down computation as the slowest node may dictate the overall computational time. A recent computational framework, called encoded optimization, creates redundancy in the data to mitigate the effect of stragglers. In this paper, we develop novel mathematical understanding for this framework demonstrating its effectiveness in much broader settings than was previously understood. We also analyze the convergence behavior of iterative encoded optimization algorithms, allowing us to characterize fundamental trade-offs between convergence rate, size of data set, accuracy, computational load (or data redundancy) and straggler toleration in this framework.

scholarly journals BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks

2021 ◽  
Vol 27 (7) ◽  
pp. 667-692
Author(s):  
Lamia Berkani ◽  
Lylia Betit ◽  
Louiza Belarif
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Recommendation Algorithms ◽  
Clustering Approach ◽  
Real World Datasets ◽  
Multiview Clustering ◽  
Improving Accuracy

Clustering-based approaches have been demonstrated to be efficient and scalable to large-scale data sets. However, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we propose in this article an optimized multiview clustering approach for the recommendation of items in social networks. First, the selection of the initial medoids is optimized using the Bees Swarm optimization algorithm (BSO) in order to generate better partitions (i.e. refining the quality of medoids according to the objective function). Then, the multiview clustering (MV) is applied, where users are iteratively clustered from the views of both rating patterns and social information (i.e. friendships and trust). Finally, a framework is proposed for testing the different alternatives, namely: (1) the standard recommendation algorithms; (2) the clustering-based and the optimized clustering-based recommendation algorithms using BSO; and (3) the MV and the optimized MV (BSO-MV) algorithms. Experimental results conducted on two real-world datasets demonstrate the effectiveness of the proposed BSO-MV algorithm in terms of improving accuracy, as it outperforms the existing related approaches and baselines.

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