An efficient single-layer dynamic semisupervised feedforward neural network clustering method with one epoch training, data dimensionality reduction, and controlling noise data abilities is discussed to overcome the problems of high training time, low accuracy, and high memory complexity of clustering. Dynamically after the entrance of each new online input datum, the code book of nonrandom weights and other important information about online data as essentially important information are updated and stored in the memory. Consequently, the exclusive threshold of the data is calculated based on the essentially important information, and the data is clustered. Then, the network of clusters is updated. After learning, the model assigns a class label to the unlabeled data by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and density of each cluster are updated. The accuracy of the proposed model is measured through the number of clusters, the quantity of correctly classified nodes, and F-measure. Briefly, in order to predict the survival time, the F-measure is 100% of the Iris, Musk2, Arcene, and Yeast data sets and 99.96% of the Spambase data set from the University of California at Irvine Machine Learning Repository; and the superior F-measure results in between 98.14% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center. We show that the proposed method is applicable in different areas, such as the prediction of the hydrate formation temperature with high accuracy.

One of the greatest potential of applying intelligent multi agent systems is the support of machine learning in order to reflect the whole complexity of the real and virtual world. However, a critical deficiency is a gap between two applicable streams of intelligent multi agent technology and learning models. In order to solve this problem, we developed a framework of intelligent multi agent based on the efficient feed-forward neural network clustering method. A feed-forward neural network is a software version of the brain and a popular tool for statistical decision making. The framework is applicable to different domains successfully and for the potential case study, the clinical domain and the breast cancer database from the University of Malaya Medical Center is considered to predict the survival time.

The aim of this research is to develop and propose a single-layer semi-supervised feed forward neural network clustering method with one epoch training in order to solve the problems of low training speed, accuracy and high time and memory complexities of clustering. A code book of non-random weights is learned through the input data directly. Then, the best match weight (BMW) vector is mined from the code book, and consequently an exclusive total threshold of each input data is calculated based on the BMW vector. The input data are clustered based on their exclusive total thresholds. Finally, the method assigns a class label to each input data by using a K-step activation function for comparing the total thresholds of the training set and the test set. The class label of other unlabeled and unknown input test data are predicted based on their clusters or trial and