Data Classification and Prediction

Author(s):  
Pudumalar S ◽  
Suriya K S ◽  
Rohini K

This chapter describes how we live in the era of data, where every event in and around us creates a massive amount of data. The greatest challenge in front of every data scientist is making this raw data, a meaningful one to solve a business problem. The process of extracting knowledge from the large database is called as Data mining. Data mining plays a wrestling role in all the application like Health care, education and Agriculture, etc. Data mining is classified predictive and descriptive model. The predictive model consists of classification, regression, prediction, time series analysis and the descriptive model consists of clustering, association rules, summarization and sequence discovery. Predictive modeling associates the important areas in the data mining called classification and prediction.

1968 ◽  
Vol 68 (10) ◽  
pp. 2135
Author(s):  
Thelma Ingles ◽  
Mildred Montag ◽  
Anne R. Sommers ◽  
Edna A. Fagan ◽  
Inez Hinsvark

2021 ◽  
Author(s):  
Matthias J. Witti ◽  
Daniel Hartmann ◽  
Birgit Wershofen ◽  
Jan M. Zottmann

1998 ◽  
Vol 5 (4) ◽  
pp. 347-356 ◽  
Author(s):  
S. W. McRoy ◽  
A. Liu-Perez ◽  
S. S. Ali

1995 ◽  
Vol 7 (3) ◽  
pp. 143-147 ◽  
Author(s):  
Deborah E. Thorpe ◽  
Christine P. Baker

2009 ◽  
Vol 32 (3) ◽  
pp. 300-313 ◽  
Author(s):  
Chato Rasoal ◽  
Tomas Jungert ◽  
Stephan Hau ◽  
Elinor Edvardsson Stiwne ◽  
Gerhard Andersson

Author(s):  
Richard Windle ◽  
Heather Wharrad

This chapter will review the definition, development and characteristics of reusable learning objects (RLOs) and outline examples of how these resources are meeting the challenges of interprofessional learning. It will discuss the ways in which pedagogy is developed and expressed within RLOs and how this may impact on interprofessionality.


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