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  Browse All Reviews > Information Systems (H) > Database Management (H.2) > Database Applications (H.2.8) > Data Mining (H.2.8...)  
 
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  1-10 of 580 Reviews about "Data Mining (H.2.8...)": Date Reviewed
  Multidimensional mining of massive text data
Zhang C., Shu K.,  Morgan&Claypool Publishers, 2019. 198 pp. Type: Book (978-1-681735-19-1)

This edited book on mining massive text data stands out by putting the concept of a “cube” center stage: a multi-dimensional space in which massive text data analysis should take place. As such, it offers discussions at the crossroads ...

Jul 13 2021
   High-utility pattern mining: theory, algorithms and applications
Fournier-Viger P., Lin J., Nkambou R., Vo B., Tseng V.,  Springer International Publishing, New York, NY, 2019. 337 pp. Type: Book (978-3-030049-20-1)

Finding frequently occurring patterns of transactions in transactional databases, such as products that customers often purchase together, can be extremely useful in identifying bestselling products and co-promoting products. However, “frequ...

Jun 24 2021
  Modern data mining algorithms in C++ and CUDA C
Masters T.,  Apress, New York, NY, 2020. 228 pp. Type: Book (978-1-484259-87-0)

In his earlier book from 2018, Data mining algorithms in C++ [1], the author indicated that a “volume 2 will appear some day.” This is it. It builds on the techniques and tools of the earlier book and follows a similar style of ...

May 5 2021
  Aspect aware learning for aspect category sentiment analysis
Zhu P., Chen Z., Zheng H., Qian T.  ACM Transactions on Knowledge Discovery from Data 13(6): 1-21, 2019. Type: Article

Do you like (or dislike) “fruit flies like a banana,” but not “time flies like an arrow”? How should humans and computerized systems accurately distinguish between predefined and undefined categories of words used in senten...

Apr 6 2021
  Evolutionary decision trees in large-scale data mining
Kretowski M.,  Springer International Publishing, New York, NY, 2019. 180 pp. Type: Book (978-3-030218-50-8)

Decision trees are valuable counterparts to neural networks in data mining--the book confirms this statement. Evolutionary decision trees in large-scale data mining is a didactic introduction to, and overview of, the almost two-decade-...

Feb 15 2021
  Learning from sets of items in recommender systems
Sharma M., Harper F., Karypis G.  ACM Transactions on Interactive Intelligent Systems 9(4): 1-26, 2019. Type: Article

Recommender systems are sets of computer algorithms or methods, implemented to provide suggestions or recommendations of relevant items to users. With the intensification of web services, from e-commerce to online advertising, recommender systems ...

Jan 25 2021
  A survey on food computing
Min W., Jiang S., Liu L., Rui Y., Jain R.  ACM Computing Surveys 52(5): 1-36, 2019. Type: Article

The authors have written an extensive survey of the published literature related to food computing. The survey is about 26 pages long, with an additional ten pages of (about 300) references....

Nov 27 2020
  Descriptive data mining (2nd ed.)
Olson D., Lauhoff G.,  Springer International Publishing, New York, NY, 2019. 130 pp. Type: Book

Data mining when applied in a business context aims to improve decision making and strategic advantages by providing a better understanding of customers, suppliers, employees, and other stakeholders. The algorithms behind data mining may be comple...

Nov 2 2020
   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
  A unified framework with multi-source data for predicting passenger demands of ride services
Wang Y., Lin X., Wei H., Wo T., Huang Z., Zhang Y., Xu J.  ACM Transactions on Knowledge Discovery from Data 13(6): 1-24, 2019. Type: Article

Ride-sharing service customers look for and deserve fair fares. However, with the use of the Internet to access competing fares when booking shared rides, how should ride-sharing providers forecast passenger demands in order to remain competitive?...

Sep 15 2020
 
 
 
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