This study addresses the problem of generating application clusters from network traffic data. These clusters are used for classification and profiling ...
This study addresses the problem of generating application clusters from network traffic data. These clusters are used for classification and profiling ...
Two data clustering techniques, Hierarchical Cluster Analysis (HCA) and Fuzzy C-Means (FCM) clustering, are used to classify sets of oral cancer cell data ...
This study addresses the problem of generating application clusters from network traffic data. These clusters are used for classification and profiling purposes ...
This paper introduces two clustering methods to be used in the analysis of network traffic. The methods are the self-or- ganizing map (SOM) and the fuzzy ...
Application of Fuzzy c-Means and Self-organizing maps for genes clustering in mouse brain microarray data analysis. Publisher: IEEE. Cite This.
Timo Lampinen, Hannu Koivisto, Tapani Honkanen: Profiling Network Applications with Fuzzy C-Means Clustering and Self-Organizing Map. FSKD 2002: 300-304.
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The Fuzzy Self-Organizing Map performs a mapping from a n-dimensional input space to one-dimensional output space.
A fuzzy Kohonen clustering network is proposed which integrates the Fuzzy c-Means (FCM) model into the learning rate and updating strategies of the Kohonen ...
K-means algorithm with Kohonen's Self Organizing map is a neural network clustering methodology that maps an n-dimensional input data to a lower dimensional ...