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  Browse All Reviews > Information Systems (H) > Information Storage And Retrieval (H.3) > Information Search And Retrieval (H.3.3) > Clustering (H.3.3...)  
 
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  1-10 of 108 Reviews about "Clustering (H.3.3...)": Date Reviewed
  Big data analytics: methods and applications
Pyne S., Rao B., Rao S.,  Springer International Publishing, New York, NY, 2016. 276 pp. Type: Book (978-8-132236-26-9), Reviews: (1 of 2)

The rapid growth of online information systems, the ease of collecting data across many users, and the potential commercial value of learning about those users have led to growing interest in methods that address data characterized by high volume ...

Sep 26 2017
  Big data analytics: methods and applications
Pyne S., Rao B., Rao S.,  Springer International Publishing, New York, NY, 2016. 276 pp. Type: Book (978-8-132236-26-9), Reviews: (1 of 2)

Big data analytics has generated much research attention in the past decade, focusing on the architectural and methodological challenges of processing enormous datasets and extreme rates of data generation and collection (two of the three “V...

Apr 26 2017
  Beyond entities: promoting explorative search with bundles
Bordino I., Lalmas M., Mejova Y., Van Laere O.  Information Retrieval 19(5): 447-486, 2016. Type: Article

Search results are usually ranked lists of documents relevant to query terms. In this paper, the entity search results are bundled with those beyond the query term, by constructing the entity network where extracted entities and pairwise entity re...

Jan 25 2017
  Co-clustering structural temporal data with applications to semiconductor manufacturing
Zhu Y., He J.  ACM Transactions on Knowledge Discovery from Data 10(4): 1-18, 2016. Type: Article

New improvements in storage, measurement, and control methods in semiconductor engineering are rapidly producing more data. Today, there are valuable tools for monitoring and gathering time-based data for manufacturing devices such as integrated c...

Nov 15 2016
  Distributed and sequential algorithms for bioinformatics
Erciyes K.,  Springer International Publishing, New York, NY, 2015. 367 pp. Type: Book (978-3-319249-64-3)

The analysis of vast amounts of biological data has the potential to significantly impact and aid further development of bioengineering processes that can be used for medical purposes such as the treatment of diseases. Traditionally most bioinform...

Sep 6 2016
  Subspace clustering of data streams: new algorithms and effective evaluation measures
Hassani M., Kim Y., Choi S., Seidl T.  Journal of Intelligent Information Systems 45(3): 319-335, 2015. Type: Article

Recently, much attention has been paid to data that evolve over time, which are usually called data streams. This paper proposes a contribution for comparing the efficiency of different existing subspace clustering algorithms, meaning algorithms t...

Jun 7 2016
  Multidisciplinary approaches to artificial swarm intelligence for heterogeneous computing and cloud scheduling
Wang J., Gong B., Liu H., Li S.  Applied Intelligence 43(3): 662-675, 2015. Type: Article

As business and scientific data have increased dramatically, distributed computing using high-speed networks has become very popular for organizations. From this perspective, this paper is interesting as it presents a security-aware model for such...

Mar 24 2016
  Mixture model averaging for clustering
Wei Y., McNicholas P.  Advances in Data Analysis and Classification 9(2): 197-217, 2015. Type: Article

Clustering is a popular task in data analysis. With a broad range of applications, various clustering approaches have been developed in the literature. These computational methods often result in different clusters from the same dataset. This is n...

Sep 30 2015
  Experiments on density-constrained graph clustering
Görke R., Kappes A., Wagner D.  Journal of Experimental Algorithmics 191.1-1.31, 2015. Type: Article

Graph clustering is the task of identifying dense sub-graphs of a given graph such that these sub-graphs are sparsely interconnected. In this paper, the authors conduct an experimental evaluation of greedy graph clustering algorithms. First, the p...

Sep 8 2015
  Subjectively interesting alternative clusterings
Kontonasios K., De Bie T.  Machine Learning 98(1-2): 31-56, 2015. Type: Article

In clustering problems, there are many unknowns and many potential desirable sets of clusters. Any complex set of data contains patterns related to many potential questions, and there are likely many potential ways to cluster data for any question...

Jun 11 2015
 
 
 
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