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      Clustered linear regression

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      Author(s)
      Ari, B.
      Güvenir, H. A.
      Date
      2002
      Source Title
      Knowledge-Based Systems
      Print ISSN
      0950-7051
      Publisher
      Elsevier
      Volume
      15
      Issue
      3
      Pages
      169 - 175
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      Clustered linear regression (CLR) is a new machine learning algorithm that improves the accuracy of classical linear regression by partitioning training space into subspaces. CLR makes some assumptions about the domain and the data set. Firstly, target value is assumed to be a function of feature values. Second assumption is that there are some linear approximations for this function in each subspace. Finally, there are enough training instances to determine subspaces and their linear approximations successfully. Tests indicate that if these approximations hold, CLR outperforms all other well-known machine-learning algorithms. Partitioning may continue until linear approximation fits all the instances in the training set - that generally occurs when the number of instances in the subspace is less than or equal to the number of features plus one. In other case, each new subspace will have a better fitting linear approximation. However, this will cause over fitting and gives less accurate results for the test instances. The stopping situation can be determined as no significant decrease or an increase in relative error. CLR uses a small portion of the training instances to determine the number of subspaces. The necessity of high number of training instances makes this algorithm suitable for data mining applications. © 2002 Elsevier Science B.V. All rights reserved.
      Keywords
      Clustering Linear Regression
      Eager Approach
      Machine Learning Algorithm
      Approximation Theory
      Data Mining
      Learning Algorithms
      Regression Analysis
      Clustered Linear Regression
      Learning Systems
      Permalink
      http://hdl.handle.net/11693/24733
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/S0950-7051(01)00154-X
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