A scalable, incremental learning algorithm for classification problems

Nong Ye, Xiangyang Li

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


In this paper a novel data mining algorithm, Clustering and Classification Algorithm-Supervised (CCA-S), is introduced. CCA-S enables the scalable, incremental learning of a non-hierarchical cluster structure from training data. This cluster structure serves as a function to map the attribute values of new data to the target class of these data, that is, classify new data. CCA-S utilizes both the distance and the target class of training data points to derive the cluster structure. In this paper, we first present problems with many existing data mining algorithms for classification problems, such as decision trees, artificial neural networks, in scalable and incremental learning. We then describe CCA-S and discuss its advantages in scalable, incremental learning. The testing results of applying CCA-S to several common data sets for classification problems are presented. The testing results show that the classification performance of CCA-S is comparable to the other data mining algorithms such as decision trees, artificial neural networks and discriminant analysis.

Original languageEnglish (US)
Pages (from-to)677-692
Number of pages16
JournalComputers and Industrial Engineering
Issue number4
StatePublished - Sep 2002


  • Classification
  • Data mining
  • Incremental learning
  • Scalability

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)


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