Fuzzy clustering-based video suggestion system with the metric of interest

In this thesis, a fuzzy and interest-based clustering method that estimates the number of clusters is proposed. The proposed Fuzzy C Means Jensen Shannon (FCMJS) method was run with an artificial data set consisting of 6 clusters and 1000 elements. The results of the study were compared with Fuzzy C Means (FCM), a fuzzy based clustering algorithm, Jensen Shannon (JS) an interest based method, Probabilistic C Means (PCM) and Probabilistic Fuzzy C Means (PFCM) methods, which are fuzzy-based probabilistic clustering methods. To evaluate the comparison results, 7 different cluster validity indices and accuracy metric were used. When the clustering results of FCMJS, PCM, PFCM, and JS were compared with the accuracy metric, the FCM and FCMJS methods were found to be more successful with 81.7059% and 81.6864% accuracy, respectively, compared to the other three methods. When the clustering ability of the method was tested using cluster validity indices, the FCM and FCMJS methods gave better results than the PCM and PFCM methods. An adaptive FCMJS method has been developed to overcome the difficulty of determining the threshold value in the FCMJS algorithm. When the adaptive FCMJS method was tested with different maximum cluster numbers, it predicted the correct number of clusters. When the clustering ability of the method was tested using cluster validity indices and accuracy metrics, the adaptive FCMJS method was found to be successful. In addition, the performance of the proposed methods was tested on a dataset created to recommend movies to users. The movie data was weighted using the Dirichlet function for action, adventure, comedy, drama, and horror genres to create a dataset containing the characteristics of these 5 movie genres. This movie dataset was clustered using FCM, FCMJS, adaptive FCMJS, PCM, PFCM, and JS, and compared in terms of accuracy metrics. In this comparison, the FCMJS method achieved a high success rate of 89.4628% and the adaptive FCMJS method achieved 89.0593% compared to other methods. Additionally, the performance of the proposed methods was compared with FCM, PCM, and PFCM methods in terms of cluster validity indices. According to the results, the FCMJS and adaptive FCMJS methods successfully grouped similar movies based on their genres by predicting the appropriate number of clusters that a user may watch