clustering methods

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

Nowadays, with the expansion of the internet and its associated technologies, recommender systems have become increasingly common. In this work, the main purpose is to apply new deep learning-based clustering methods to overcome the data sparsity problem and increment the efficiency of recommender systems based on precision, accuracy, F-measure, and recall. Within the suggested model of this research, the hidden biases and input weights values of the extreme learning machine algorithm are produced by the Restricted Boltzmann Machine and then clustering is performed. Also, this study employs the ELM for two approaches, clustering of training data and determine the clusters of test data. The results of the proposed method evaluated in two prediction methods by employing average and Pearson Correlation Coefficient in the MovieLens dataset. Considering the outcomes, it can be clearly said that the suggested method can overcome the problem of data sparsity and achieve higher performance in recommender systems. The results of evaluation of the proposed approach indicate a higher rate of all evaluation metrics while using the average method results in rates of precision, accuracy, recall, and F-Measure come to 80.49, 83.20, 67.84 and 73.62 respectively.

Permeability, the ability of rocks to flow hydrocarbons, is directly determined from core. Due to high cost associated with coring, many techniques have been suggested to predict permeability from the easy-to-obtain and frequent properties of reservoirs such as log derived porosity. This study was carried out to put clustering methods (dynamic clustering (DC), ascending hierarchical clustering (AHC) self organizing map (SOM) and multi-resolution graph-based clustering (MRGC)) into practice in order to predict the permeability of a heterogeneous carbonate reservoir in southwest of Iran. In addition, the results are compared with three conventional approaches, empirical models, regression analysis, and ANN. The performance of all the examined methods was compared in order to choose the best approach for predicting permeability in un-cored wells of the studied field. For all clustering methods, selecting the optimal number of clusters is the most important task. The optimal values for the number of clusters are selected by iteration. The optimal number of clusters for MRGC, SOM, DC, and AHC are 7, 9, 9, and 8 respectively. Empirical equations and regression analysis weakly predict permeability and the value of R2 parameters of both approaches are around 0.6. Generally the performance of clustering techniques is acceptable in Fahliyan formation. These techniques predict permeability between 1 and 1000 mD very well and just overestimate permeability values less than 1 mD. SOM performed the best among all examined techniques (R2=0.7911). The constructed and validated SOM model with 9 clusters was selected to predict permeability in one of un-cored wells of the studied field. In this well, the predicted permeability was in good agreement with MDT derived permeability.