الفهرس 
An Alternative approach for clustering using max-plus algebra
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Abstract
Clustering is one of the main research directions in data mining (DM) based on
unsupervised learning. It aims at partitioning a given set of objects into
homogenous groups such that the objects of the same cluster share similar
characteristics (high intra-cluster similarity or cohesiveness within clusters),
relative to those belonging to different clusters (low inter-cluster similarity or
distinctiveness between clusters). The concept of min-plus algebra (
* + ), or (
), involves combining two operators ―min‖ and
―plus‖ on a basic set . The minimum operator
chooses the smaller one
from two elements, * + The plus operator
adds two
elements, . It is most natural to associate Euclidean distance
metric with the min-plus metric in order to take the minimum of all distances
between a data point towards all cluster centroids. The thrust of the proposed
work is to determine which measure is suitable for data clustering amongst 12
distance/similarity measures.