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This paper presents a method for hidden pattern mining on dental medical records related to oral conditions and different procedures that are performed on various patients. The decision to follow a set of procedures is based on the examination and diagnostics. Nowadays there is an increasing trend towards digital dentistry, but the full potential of digital data is not yet exploited because of several...
Clustering is a technique that can divide data objects into meaningful groups. Particle swarm optimization is an evolutionary computation technique developed through a simulation of simplified social models. K-means is one of the popular unsupervised learning clustering algorithms. After analyzing particle swarm optimization and K-means algorithm, a new hybrid algorithm based on both algorithms is...
The world of ants is a reach source of inspiration since real ants are able to solve collectively relatively complex problems. Particularly, several ant based clustering algorithms have been proposed in the literature. These clustering models were derived from several phenomena among real ants such as cemetery organization, recognition system, building alive structures, etc. In this work, we try to...
An unsupervised machine learning method-clustering, is introduced to conclude characteristics of vessel traffic flow data. A new way is found to implement data analysis in vessel traffic field using artificial intelligent technique. A similarity based algorithm, K-means, is selected in the clustering process for its simplicity and efficiency and a popular data mining tool named WEKA is chosen to execute...
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