Fast and accuracy control chart pattern recognition using a new cluster-k-nearest neighbor

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

By taking advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less subclass number, stability and bounded time of classification with respect to the variable data size. We find between 96% and 99.7 % of accuracy in the classification of 6 different types of Time series by using K-means cluster algorithm and we find 99.7% by using the new clustering algorithm.

Original languageEnglish
Pages (from-to)1142-1146
Number of pages5
JournalWorld Academy of Science, Engineering and Technology
Volume37
Publication statusPublished - Jan 2009
Externally publishedYes

Keywords

  • Classification
  • Gaussian mixture model
  • Pattern recognition
  • Time series
  • k-Nearest neighbor
  • k-means cluster

Fingerprint

Dive into the research topics of 'Fast and accuracy control chart pattern recognition using a new cluster-k-nearest neighbor'. Together they form a unique fingerprint.

Cite this