An efficient distributed hierarchical-clustering algorithm for large scale data

Tang, Cheng-Hsien; Huang, An-Ching; Tsai, Meng‐Feng; Wang, Wei-Jen

doi:10.1109/compsym.2010.5685388

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…The primary purpose of this method is to group several data or objects into groups (clusters) which in each cluster will obtain data that has similarities [23]. Hierarchical clustering is one method of clustering [24][25], [26]. According to [22], the hierarchical clustering algorithm provides hierarchical clusters, and the classification of clusters depends on a bottom-up or top-down style which was formed by hierarchical decomposition.…”

Section: Hierarchical Clusteringmentioning

confidence: 99%

Hierarchical Clustering for Functionalities E-Commerce Adoption

Triandini

Hermawati

Suniantara

2020

kursor

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Web functionality is one driver for e-commerce adoption. It is appeared the level of technological capabilities as well as the accentuation of the strategy put on e-commerce by the organization. Web functionality is related to the level of e-commerce relocation. Website with more functionality will give way better benefits for shoppers and trade partners. Functionalities of web are components that support the achievement of adoption benefits. Hierarchical clustering and ranking availability of e-commerce functionality is a challenging task. Ward Linkage algorithm was used to measure distance. This study proposed to get a grouping of e-commerce functionalities that influence e-commerce adoption and to get the ranking of the groups that most influence the achievement of these benefits. Result shows that functionalities that supports the achievement of every benefit of e-commerce has been clustered into two or three clusters, where each cluster also has been ranked to facilitate the achievement of these benefits

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Section: Hierarchical Clusteringmentioning

confidence: 99%

Hierarchical Clustering for Functionalities E-Commerce Adoption

Triandini

Hermawati

Suniantara

2020

kursor

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“…A hierarchical clustering algorithm was proposed by Tang et al 11 for a distributed environment. The algorithm computes a similarity matrix in parallel for the data items.…”

Section: Related Workmentioning

confidence: 99%

A scalable parallel algorithm for building web directories

Seshadri

Maruthappan

Raman

2020

Concurrency and Computation

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Summary Web directories like Wikipedia and Open Directory Mozilla facilitate efficient information retrieval (IR) of web documents from a huge web corpus. Maintenance of these web directories is understandably a difficult task that requires manual curation by human editors or semi‐automated mechanisms. Research on parallel algorithms for the automated curation of these web directories will be beneficial to the IR domain. Hence, in this article, we propose a parallel algorithm for automatically creating web directories from a corpus of web‐documents. We have used centrality‐based techniques to split the corpus into fine‐grained clusters and subsequently an agglomeration based on locality sensitive hashing to identify coarse‐grained clusters in the web‐directory. Experimental results show that the algorithm generates meaningful hierarchies of the input corpus as measured by cluster‐validity indices, like F‐measure, rand index, and cluster purity. The algorithm achieves a significant speedup and scales well both with the number of processors and the size of the input corpus.

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