crow search algorithm

Data clustering is an ideal way of working with a huge amount of data and looking for a structure in the dataset. In other words, clustering is the classification of the same data; the similarity among the data in a cluster is maximum and the similarity among the data in the different clusters is minimal. The innovation of this paper is a clustering method based on the Crow Search Algorithm (CSA) and Opposition-based Learning (OBL). The CSA is one of the meat-heuristic algorithms that is difficult at the exploration and exploitation stage, and thus, the clustering problem is susceptible to initialization for centrality of the clusters. In the proposed model, the crows change their position based on the OBL method. The position of the crows is updated using OBL to find the best position for the cluster. To evaluate the performance of the proposed model, the experiments were performed on 8 datasets from the UCI repository and compared with seven different clustering algorithms. The results show that the proposed model is more accurate, more efficient, and more robust than other clustering algorithms. Also, the convergence of the proposed model is better than other algorithms.

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial Intelligence (AI) and optimization algorithms which are highly potential in Feature Selection (FS) and words extraction. In this paper Crow Search Algorithm (CSA) is used for FS and K-Nearest Neighbor (KNN) for classification. Additionally, TF technique is proposed for counting words and calculating the words’ frequency. Analysis is performed on Reuters-21578, Webkb and Cade 12 datasets. The results indicate that the proposed model is more accurate in classification than KNN model and, show greater F-Measure compared to KNN and C4.5. Moreover, by using FS, the proposed model promotes classification accuracy by %27, compared to KNN.