Behaviour Analysis Model for Social Networks using Genetic Weighted Fuzzy C-Means Clustering and Neuro-Fuzzy Classifier

摘要

Genetic algorithms are helpful to make effective decisions using suitable fitness functions. They can be used to perform both clustering and classification. However, Clustering algorithms enhanced only with genetic operators are not sufficient for making decision in many critical applications. In this study, researchers propose a new user behaviour analysis model by combining Genetic algorithm with Weighted Fuz2y C-Means Clustering Algorithm (GNWFCMA) for effective clustering. The proposed clustering algorithm is used to improve the classification accuracy by providing initial groups. In addition, researchers use a five factor analysis also for effective clustering. Finally, researchers use a neuro-fuzzy classifier for classifying the data. The experimental results obtained from this study shows that the clustering results when combined with classification algorithm provides better classification accuracy when tested with Weblog dataset.