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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 55 Reviews about "Clustering (H.3.3...)": Date Reviewed
  Data clustering: theory, algorithms, and applications
Gan G., Ma C., Wu J., SIAM, Philadelphia, PA, 2020. 406 pp.  Type: Book (978-1-611976-32-8)

Data clustering is an unsupervised method of grouping data such that objects in the same cluster are similar and objects in different clusters are distinct. Such techniques have a very diverse span of applicability in areas such as art...

Mar 31 2022
  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 professio...

Oct 7 2021
  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 an...

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
  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 s...

Jan 18 2019
  A viewable indexing structure for the interactive exploration of dynamic and large image collections
Rayar F., Barrat S., Bouali F., Venturini G. ACM Transactions on Knowledge Discovery from Data 12(1): 1-26, 2018.  Type: Article

The authors are interested in developing a method for building and indexing a large collection of images and providing a means for studying these images. Their interest is in assembling a collection of images that are arranged accordin...

May 31 2018
  Fast and accurate time-series clustering
Paparrizos J., Gravano L. ACM Transactions on Database Systems 42(2): 1-49, 2017.  Type: Article

Clustering temporal data, namely time series, is a challenging and expensive computational task in terms of accuracy and speed. Despite the fact that a wide variety of time-series clustering algorithms exist in the literature, they rem...

Apr 16 2018
  On temporal-constrained sub-trajectory cluster analysis
Pelekis N., Tampakis P., Vodas M., Doulkeridis C., Theodoridis Y. Data Mining and Knowledge Discovery 31(5): 1294-1330, 2017.  Type: Article

The growing popularity of location-enabled tracking devices has triggered a new sense of interest and enthusiasm for creating appropriate datasets or databases, and new approaches for carrying out data analytics. Demand and need for de...

Apr 12 2018
  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 ...

Jun 7 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 datase...

Sep 30 2015
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