Text clustering problem (TCP) is a leading process in many key areas such as information retrieval, text mining, and natural language processing. This presents the need for a potent document clustering algorithm that can be used effectively to navigate, summarize, and arrange information to congregate large data sets. This paper encompasses an adaptation of the grey wolf optimizer (GWO) for TCP, referred to as TCP-GWO. The TCP demands a degree of accuracy beyond that which is possible with metaheuristic swarm-based algorithms. The main issue to be addressed is how to split text documents on the basis of GWO into homogeneous clusters that are sufficiently precise and functional. Specifically, TCP-GWO, or referred to as the document clustering algorithm, used the average distance of documents to the cluster centroid (ADDC) as an objective function to repeatedly optimize the distance between the clusters of the documents. The accuracy and efficiency of the proposed TCP-GWO was demonstrated on a sufficiently large number of documents of variable sizes, documents that were randomly selected from a set of six publicly available data sets. Documents of high complexity were also included in the evaluation process to assess the recall detection rate of the document clustering algorithm. The experimental results for a test set of over a part of 1300 documents showed that failure to correctly cluster a document occurred in less than 20% of cases with a recall rate of more than 65% for a highly complex data set. The high F-measure rate and ability to cluster documents in an effective manner are important advances resulting from this research. The proposed TCP-GWO method was compared to the other well-established text clustering methods using randomly selected data sets. Interestingly, TCP-GWO outperforms the comparative methods in terms of precision, recall, and F-measure rates. In a nutshell, the results illustrate that the proposed TCP-GWO is able to excel compared to the other comparative clustering methods in terms of measurement criteria, whereby more than 55% of the documents were correctly clustered with a high level of accuracy.

The conventional information retrieval (IR) framework consists of four primary phases, namely, pre-processing, indexing, querying and retrieving results. Some phases of the current Arabic IR (AIR) framework have several drawbacks. This research aims to enhance an AIR by improving the processes in a conventional IR framework. We introduce an enhanced stop-word list in the pre-processing level and investigate several Arabic stemmers. In addition, an Arabic WordNet was utilized in the corpus and query expansion levels. We also adopted semantic information for the Pseudo Relevance Feedback. The enhanced Arabic IR framework was built and evaluated using TREC 2001 data. The technique of using the Arabic WordNet to build a semantic relationship between query and corpus in two levels, that is, the corpus and query levels, is a new one. The enhanced AIR framework demonstrated an improvement by 49% in terms of mean average precision, with an increase of 7.3% in recall compared with the baseline framework.

The major aim of classification is to extract categories of inputs according to their characteristics. The literature contains several methods that aim to solve the time series classification problem, such as the artificial neural network (ANN) and the support vector machine (SVM). Time series classification is a supervised learning method that maps the input to the output using historical data. The primary objective is to discover interesting patterns hidden in the data. In this study, we use a new method called SVNN which combines the SVM and ANN classification techniques to solve the time series data classification problem. The proposed SVNN is applied to six benchmark UCR time series datasets. The results show that the proposed method outperforms the ANN and SVM on all datasets. Further comparison with other approaches in the literature also shows that the SVNN is able to maximise accuracy. It is believed that combining classification techniques can give better results in terms of accuracy and better solutions for time series classification.