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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...)  
  1-10 of 123 Reviews about "Clustering (H.3.3...)": Date Reviewed
  Mathematics of data science: a computational approach to clustering and classification
Calvetti D., Somersalo E.,  SIAM, Philadelphia, PA, 2020. 189 pp. Type: Book (978-1-611976-36-6)

Mathematics is the foundation of data science techniques. With the democratization of data science, almost anyone has access to easy-to-use tools and platforms to get started with data science applications. However, a serious professional or resea...

Oct 7 2021
  Fuzzy collaborative forecasting and clustering: methodology, system architecture, and applications
Chen T., Honda K.,  Springer International Publishing, New York, NY, 2019. 100 pp. Type: Book (978-3-030225-73-5)

Collaborative machine learning (ML)--sometimes known as federated ML--has been gaining momentum in the last few years for several in-demand reasons, including privacy-preserving data analysis, sharing complex computations (such as those ...

Jun 14 2021
   Fundamentals of image data mining: analysis, features, classification and retrieval
Zhang D.,  Springer International Publishing, New York, NY, 2019. 314 pp. Type: Book (978-3-030179-88-5)

Data mining is “the science of extracting useful knowledge from huge data repositories” [1]. Extending this definition, it can be concluded that image data mining is the science of extracting valuable knowledge from large volumes of im...

Sep 21 2020
  Data analysis in bi-partial perspective: clustering and beyond
Owsinski J.,  Springer International Publishing, New York, NY, 2020. 153 pp. Type: Book (978-3-030133-88-7)

Owsinski’s book provides an interesting “bi-partial” strategy for analyzing data; it is not only uniquely general, but also successful in building several useful methods to tackle issues related to data analysis, including algori...

Jun 24 2020
   A rapid hybrid clustering algorithm for large volumes of high dimensional data
Rathore P., Kumar D., Bezdek J., Rajasegarar S., Palaniswami M.  IEEE Transactions on Knowledge and Data Engineering 31(4): 641-654, 2019. Type: Article

FensiVAT is a rapid hybrid clustering algorithm that identifies clusters in large datasets characterized by many instances (N) and multiple features (p) in each instance....

Mar 10 2020
  Concepts and methods for a librarian of the web
Kubek M.,  Springer International Publishing, New York, NY, 2020. 173 pp. Type: Book (978-3-030231-35-4)

“A librarian of the web” is an interesting proposition that might, when fully implemented, help people find documents they are looking for in a more effective way. The first two chapters start by introducing library services, the key r...

Mar 4 2020
  Centrality and diversity in search: roles in A.I., machine learning, social networks, and pattern recognition
Murty M., Biswas A.,  Springer International Publishing, New York, NY, 2019. 94 pp. Type: Book (978-3-030247-12-6)

This book is part of the “SpringerBriefs in Intelligent Systems” series, which covers diverse areas of intelligent systems, including artificial intelligence (AI), multiagent systems, and cognitive robotics. From their web profiles I g...

Dec 10 2019
  Feature selection and enhanced krill herd algorithm for text document clustering
Abualigah L.,  Springer International Publishing, New York, NY, 2019. 165 pp. Type: Book (978-3-030106-73-7)

This monograph, which comes out of the author’s PhD thesis, studies text document clustering with the help of the krill herd (KH) algorithm....

May 10 2019
  An enhancement of computer aided approach for colon cancer detection in WCE images using ROI based color histogram and SVM2
Shanmuga Sundaram P., Santhiyakumari N.  Journal of Medical Systems 43(2): 1-8, 2019. Type: Article

The authors describe a method to process the information gathered in wireless capsule endoscopy (WCE) images and ultimately determine whether a region of interest (ROI) is benign, malignant, or normal....

Mar 29 2019
  Triclustering algorithms for three-dimensional data analysis: a comprehensive survey
Henriques R., Madeira S.  ACM Computing Surveys 51(5): 1-43, 2018. Type: Article

The rapid increase in data streams poses significant challenges to their interpretation. Algorithms increasingly target 3D datasets, which plot observations, attributes, and contexts, to capture patterns in the fields of medicine and social networ...

Jan 18 2019
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