State of the art document clustering algorithms based on semantic similarity

The constant success of the Internet made the number of text documents in electronic forms increases hugely. The techniques to group these documents into meaningful clusters are becoming critical missions. The traditional clustering method was based on statistical features, and the clustering was done using a syntactic notion rather than semantically. However, these techniques resulted in un-similar data gathered in the same group due to polysemy and synonymy problems. The important solution to this issue is to document clustering based on semantic similarity, in which the documents are grouped according to the meaning and not keywords. In this research, eighty papers that use semantic similarity in different fields have been reviewed; forty of them that are using semantic similarity based on document clustering in seven recent years have been selected for a deep study, published between the years 2014 to 2020. A comprehensive literature review for all the selected papers is stated. Detailed research and comparison regarding their clustering algorithms, utilized tools, and methods of evaluation are given. This helps in the implementation and evaluation of the clustering of documents. The exposed research is used in the same direction when preparing the proposed research. Finally, an intensive discussion comparing the works is presented, and the result of our research is shown in figures

Self-adaptive GA, quantitative semantic similarity measures and ontology-based text clustering

As the common clustering algorithms use vector space model (VSM) to represent document, the conceptual relationships between related terms which do not co-occur literally are ignored. A genetic algorithm-based clustering technique, named GA clustering, in conjunction with ontology is proposed in this article to overcome this problem. In general, the ontology measures can be partitioned into two categories: thesaurus-based methods and corpus-based methods. We take advantage of the hierarchical structure and the broad coverage taxonomy of Wordnet as the thesaurus-based ontology. However, the corpus-based method is rather complicated to handle in practical application. We propose a transformed latent semantic analysis (LSA) model as the corpus-based method in this paper. Moreover, two hybrid strategies, the combinations of the various similarity measures, are implemented in the clustering experiments. The results show that our GA clustering algorithm, in conjunction with the thesaurus-based and the LSA-based method, apparently outperforms that with other similarity measures. Moreover, the superiority of the GA clustering algorithm proposed over the commonly used k-means algorithm and the standard GA is demonstrated by the improvements of the clustering performance

Experiments in Clustering Homogeneous XML Documents to Validate an Existing Typology

This paper presents some experiments in clustering homogeneous XMLdocuments to validate an existing classification or more generally anorganisational structure. Our approach integrates techniques for extracting knowledge from documents with unsupervised classification (clustering) of documents. We focus on the feature selection used for representing documents and its impact on the emerging classification. We mix the selection of structured features with fine textual selection based on syntactic characteristics.We illustrate and evaluate this approach with a collection of Inria activity reports for the year 2003. The objective is to cluster projects into larger groups (Themes), based on the keywords or different chapters of these activity reports. We then compare the results of clustering using different feature selections, with the official theme structure used by Inria.Comment: (postprint); This version corrects a couple of errors in authors' names in the bibliograph

Taming Wild High Dimensional Text Data with a Fuzzy Lash

The bag of words (BOW) represents a corpus in a matrix whose elements are the frequency of words. However, each row in the matrix is a very high-dimensional sparse vector. Dimension reduction (DR) is a popular method to address sparsity and high-dimensionality issues. Among different strategies to develop DR method, Unsupervised Feature Transformation (UFT) is a popular strategy to map all words on a new basis to represent BOW. The recent increase of text data and its challenges imply that DR area still needs new perspectives. Although a wide range of methods based on the UFT strategy has been developed, the fuzzy approach has not been considered for DR based on this strategy. This research investigates the application of fuzzy clustering as a DR method based on the UFT strategy to collapse BOW matrix to provide a lower-dimensional representation of documents instead of the words in a corpus. The quantitative evaluation shows that fuzzy clustering produces superior performance and features to Principal Components Analysis (PCA) and Singular Value Decomposition (SVD), two popular DR methods based on the UFT strategy