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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.

We developed an efficient semisupervised feedforward neural network clustering model with one epoch training and data dimensionality reduction ability to solve the problems of low training speed, accuracy, and high memory complexity of clustering. During training, a codebook of nonrandom weights is learned through input data directly. A standard weight vector is extracted from the codebook, and the exclusive threshold of each input instance is calculated based on the standard weight vector. The input instances are clustered based on their exclusive thresholds. The model assigns a class label to each input instance through the training set. The class label of each unlabeled input instance is predicted by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and the density of each cluster are updated. The accuracy of the proposed model was measured through the number of clusters and the quantity of correctly classified nodes, which was 99.85%, 100%, and 99.91% of the Breast Cancer, Iris, and Spam data sets from the University of California at Irvine Machine Learning Repository, respectively, and the superiorFmeasure results between 98.29% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center to predict the survival time.

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.

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