Logo

Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor

Brahim Belhaouari, samir (2009) Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor. [Citation Index Journal]

This is the latest version of this item.

[img] PDF
Restricted to Registered users only

165Kb

Abstract

By taking advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classi¯cation, 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 classi¯cation with respect to the variable data size. We ¯nd between 96% and 99.7 % of accuracy in the classi¯cation of 6 di®erent types of Time series by using K-means cluster algorithm and we ¯nd 99.7% by using the new clustering algorithm.

Item Type:Citation Index Journal
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:2719
Deposited By: Dr Samir Brahim Belhaouari
Deposited On:12 Jun 2011 04:31
Last Modified:19 Jan 2017 08:25

Available Versions of this Item

Repository Staff Only: item control page

Document Downloads

More statistics for this item...