Homogeneous Vs. Heterogeneous Distributed Data Clustering: A Taxonomy

2019 ◽  
pp. 51-66
Author(s):  
Rasha Kashef ◽  
Marium Warraich
Keyword(s):  
Data Clustering ◽  
Distributed Data

Distributed data clustering in sensor networks

2011 ◽  
Vol 24 (5) ◽  
pp. 207-222 ◽  
Author(s):  
Ittay Eyal ◽  
Idit Keidar ◽  
Raphael Rom
Keyword(s):  
Sensor Networks ◽  
Data Clustering ◽  
Distributed Data

Distributed data clustering can be efficient and exact

2000 ◽  
Vol 2 (2) ◽  
pp. 34-38 ◽  
Author(s):  
George Forman ◽  
Bin Zhang
Keyword(s):  
Data Clustering ◽  
Distributed Data

Distributed Data Clustering

2015 ◽  
Vol 21 (1) ◽  
pp. 19-21
Author(s):  
Sundas Charagh ◽  
◽  
Ayesha Saleem ◽  
Amnah Mukhtar
Keyword(s):  
Data Clustering ◽  
Distributed Data

Distributed Data Clustering Using Mobile Agents and EM Algorithm

2016 ◽  
Vol 10 (1) ◽  
pp. 281-289 ◽  
Author(s):  
Behrooz Safarinejadian ◽  
Kazem Hasanpour
Keyword(s):  
Em Algorithm ◽  
Data Clustering ◽  
Mobile Agents ◽  
Distributed Data

scholarly journals Enhancing Privacy in Distributed Data Clustering

2012 ◽  
Vol 26 (2) ◽  
Author(s):  
Luong The Dung ◽  
Ho Tu Bao
Keyword(s):  
Data Clustering ◽  
Distributed Data

Distributed Data Clustering Using Expectation Maximization Algorithm

2009 ◽  
Vol 9 (5) ◽  
pp. 854-864 ◽  
Author(s):  
B. Safarineja ◽  
M.B. Menhaj ◽  
M. Karrari
Keyword(s):  
Expectation Maximization ◽  
Data Clustering ◽  
Expectation Maximization Algorithm ◽  
Distributed Data

A Framework for Designing Recommender System for Consumers Using Distributed Data Clustering

2011 ◽  
pp. 253-270
Author(s):  
Flavius L. Gorgônio ◽  
José P. Araújo Neto ◽  
Taciano M. Silva
Keyword(s):  
Recommender Systems ◽  
Data Clustering ◽  
High Volume ◽  
Product Evaluation ◽  
Distributed Data ◽  
Specific Product ◽  
Additional Information ◽  
Web Social ◽  
Quality Of Products

In the past, consumers looked for information about quality of products and services with family members, friends, vendors, and experts. Currently, this reality is changing, and the number of consumers using Internet to find this kind of information is increasing, but not only to obtain additional information about a specific product, but to compare its features with other similar products. However, Internet provides a considerable amount of information through high volume of commercial sites, making the search for really useful information costly and difficult. Recommender systems are a Web social based process, performed by ordinary people, where users want to describe their degree of appreciation about items (products, services or people) based on their personal experience. This chapter proposes a framework for designing Web recommender systems that combine a meta-search engine and a data clustering strategy for product evaluation, enabling consumers to decide which products should be chosen.

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