An Unsupervised Feed Forward Neural Network Method for
Efficient Clustering
Roya Asadi1,
Sameem Abdul Kareem1, Mitra Asadi2, Shokoofeh Asadi3
1 Department of
Artificial Intelligence, University of Malaya, Malaysia
2Department of
Research, Iranian Blood Transfusion Organization, Iran
3Department of
Agreecultural Management Engineering, Iran
Abstract: This
paper presents a Real Unsupervised Feed Forward Neural Network (RUFFNN)
clustering method with one epoch training and data dimensionality reduction
ability to overcome some critical problems such as low training speed, low
accuracy as well as high memory complexity in this area. The RUFFNN method
trains a code book of real weights by utilizing input data directly without
using any random values. The Best Match Weight (BMW) vector is mined from the
weight codebook and consequently the Total Threshold (TT) of each input data is
computed based on the BMW. Finally, the input data are clustered based on their
exclusive TT. For evaluation purposes, the clustering performance of the RUFFNN
was compared to several related clustering methods using various data sets. The
accuracy of the RUFFNN was measured through the number of clusters and the
quantity of Correctly Classified Nodes.The superior clustering accuracies of
96.63%, 96.67% and 59.36% were for the Breast Cancer, Iris and Spam datasets
from the UCI repository respectively. The memory complexity of the proposed
method was O(m.n.sm) based on the number of nodes, attributes and size of the
attribute.
Keywords: Artificial
neural network, feed forward neural network, unsupervised learning, clustering,
real weight.
Recieved December 2, 2014; accepted
Augest 16, 2015