Nowadays, various online resources are growing and disseminating rapidly. In order to organize these resources, attempts have been made to use automatic Classification, which often uses statistical algorithms and machine learning. Recently, attention has been drawn to the use of library Classifications. The main challenge here is that Classification is an abstract, thought-provoking process, and machine techniques and artificial intelligence have not yet been able to completely replace the human mind. In this paper, we provide an overview of the importance of automatic Classification, machine learning, and practical algorithms and techniques of clustering and Classification like K-nearest neighbor, Bayesian models, artificial neural networks, deep learning, and hybrid Classifications. Also, the steps of automatic Classification of web pages and the techniques used in each step were mentioned. Achieving a clearer understanding of automatic Classification will enable LIS experts to communicate with experts in the field of artificial intelligence and computers. This could pave the way for interdisciplinary research.

Text categorization is one of the well studied problems in data mining and information retrieval.Given a large quantity of documents in a data set where each document is associated with its corresponding category. This research proposes a novel approach for English and Persian documents Classification with using novel method that combined competitive neural Text categorizer with new vectors that we called, string vectors. Traditional approaches to Text categorization require encoding documents into numerical vectors which leads to the two main problems: huge dimensionality and sparse distribution. Although many various feature selection methods are developed to address the first problem, the reduced dimension remains still large. If the dimension is reduced excessively by a feature selection method, robustness of document categorization is degraded. The idea of this research as the solution to the problems is to encode the documents into string vectors and apply it to the novel competitive neural Text categorizer as a string vector. Extensive experiments based on several benchmarks are conducted. The results indicated that this method can significantly improve the performance of documents Classification up to 13.8% in comparison to best traditional algorithm on standard Reuter 21578 dataset.