Non-parametric document clustering by ensemble methods

Los sesgos de los algoritmos individuales para clustering no paramétrico de documentos pueden conducir a soluciones no óptimas. Los métodos de consenso podrían compensar esta limitación, pero no han sido probados sobre colecciones de documentos. Este artículo presenta una comparación de estrategias para clustering no paramétrico de documentos por consenso. / The biases of individual algorithms for non-parametric document clustering can lead to non-optimal solutions. Ensemble clustering methods may overcome this limitation, but have not been applied to document collections. This paper presents a comparison of strategies for non-parametric document ensemble clustering.Peer ReviewedPostprint (published version

Text Categorization of Documents using K-Means and K-Means++ Clustering Algorithm

Text categorization is the technique used for sorting a set of documents into categories from a predefined set. Text categorization is useful in better management and retrieval of the text documents and also makes document retrieval as a simple task. Clustering is an unsupervised learning technique aimed at grouping a set of objects into clusters. Text document Clustering means clustering of related text documents into groups based upon their content. Various clustering algorithms are available for text categorization. This paper presents categorization of the text documents using two clustering algorithms namely K-means and K-means++ and a comparison is carried out to find which algorithm is best for categorizing text documents. This project also introduces pre-processing phase, which in turn includes tokenization, stop-words removal and stemming. It also involves Tf-Idf calculation. In addition, the impact of the three distance/similarity measures (Cosine Similarity, Jaccard coefficient, Euclidean distance) on the results of both clustering algorithms(K-means and K-means++) are evaluated. The dataset considered for evaluation consists of 600 text documents of three different categories- Festivals, Sports and Tourism in India. Our observation shows that for categorizing the text documents using K-Means++ clustering algorithm with Cosine Similarity measure gives better result as compared to K-means. For K-Means++ algorithm using Cosine Similarity measure purity of the cluster obtained is 0.8216

Incorporating semantic and syntactic information into document representation for document clustering

Document clustering is a widely used strategy for information retrieval and text data mining. In traditional document clustering systems, documents are represented as a bag of independent words. In this project, we propose to enrich the representation of a document by incorporating semantic information and syntactic information. Semantic analysis and syntactic analysis are performed on the raw text to identify this information. A detailed survey of current research in natural language processing, syntactic analysis, and semantic analysis is provided. Our experimental results demonstrate that incorporating semantic information and syntactic information can improve the performance of our document clustering system for most of our data sets. A statistically significant improvement can be achieved when we combine both syntactic and semantic information. Our experimental results using compound words show that using only compound words does not improve the clustering performance for our data sets. When the compound words are combined with original single words, the combined feature set gets slightly better performance for most data sets. But this improvement is not statistically significant. In order to select the best clustering algorithm for our document clustering system, a comparison of several widely used clustering algorithms is performed. Although the bisecting K-means method has advantages when working with large datasets, a traditional hierarchical clustering algorithm still achieves the best performance for our small datasets

The Use of Latent Semantic Indexing to Cluster Documents into Their Subject Areas

Keyword matching information retrieval systems areplagued with problems of noise in the document collection, arising from synonymy and polysemy. This noise tends to hide the latent structure of the documents, hence reduing the accuracy of the information retrieval systems, as well asmaking it difficult for clustering algorithms to pick up on shared concepts, and effectively cluster similar documents. Latent Semantic Analysis (LSA) through its use of Singular Value Decomposition reduces the dimension of the document space, mapping it onto a smaller concept space devoid of this noice and making it easier to group similar documents together. This work is an exploratory report of the use of LSA to cluster a small dataset of documents according to their topic areas to see how LSA would fare in comparison to clustering with a clustering package, without LS

Pre Processing Techniques for Arabic Documents Clustering

Clustering of text documents is an important technique for documents retrieval. It aims to organize documents into meaningful groups or clusters. Preprocessing text plays a main role in enhancing clustering process of Arabic documents. This research examines and compares text preprocessing techniques in Arabic document clustering. It also studies effectiveness of text preprocessing techniques: term pruning, term weighting using (TF-IDF), morphological analysis techniques using (root-based stemming, light stemming, and raw text), and normalization. Experimental work examined the effect of clustering algorithms using a most widely used partitional algorithm, K-means, compared with other clustering partitional algorithm, Expectation Maximization (EM) algorithm. Comparison between the effect of both Euclidean Distance and Manhattan similarity measurement function was attempted in order to produce best results in document clustering. Results were investigated by measuring evaluation of clustered documents in many cases of preprocessing techniques. Experimental results show that evaluation of document clustering can be enhanced by implementing term weighting (TF-IDF) and term pruning with small value for minimum term frequency. In morphological analysis, light stemming, is found more appropriate than root-based stemming and raw text. Normalization, also improved clustering process of Arabic documents, and evaluation is enhanced

Clustering Multiple Contextually Related Heterogeneous Datasets

Traditional clustering is typically based on a single feature set. In some domains, several feature sets may be available to represent the same objects, but it may not be easy to compute a useful and effective integrated feature set. We hypothesize that clustering individual datasets and then combining them using a suitable ensemble algorithm will yield better quality clusters compared to the individual clustering or clustering based on an integrated feature set. We present two classes of algorithms to address the problem of combining the results of clustering obtained from multiple related datasets where the datasets represent identical or overlapping sets of objects but use different feature sets. One class of algorithms was developed for combining hierarchical clustering generated from multiple datasets and another class of algorithms was developed for combining partitional clustering generated from multiple datasets. The first class of algorithms, called EPaCH, are based on graph-theoretic principles and use the association strengths of objects in the individual cluster hierarchies. The second class of algorithms, called CEMENT, use an EM (Expectation Maximization) approach to progressively refine the individual clusterings until the mutual entropy between them converges toward a maximum. We have applied our methods to the problem of clustering a document collection consisting of journal abstracts from ten different Library of Congress categories. After several natural language preprocessing steps, both syntactic and semantic feature sets were extracted. We present empirical results that include the comparison of our algorithms with several baseline clustering schemes using different cluster validation indices. We also present the results of one-tailed paired emph{T}-tests performed on cluster qualities. Our methods are shown to yield higher quality clusters than the baseline clustering schemes that include the clustering based on individual feature sets and clustering based on concatenated feature sets. When the sets of objects represented in two datasets are overlapping but not identical, our algorithms outperform all baseline methods for all indices

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

Biomedical ontology MeSH improves document clustering qualify on MEDLINE articles: A comparison study

19th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2006, Salt Lake City, UTDocument clustering has been used for better document retrieval, document browsing, and text mining. In this paper, we investigate if biomedical ontology MeSH improves the clustering quality for MEDLINE articles. For this investigation, we perform a comprehensive comparison study of various document clustering approaches such as hierarchical clustering methods (single-link, complete-link, and complete link), Bisecting K-means, K-means, and Suffix Tree Clustering (STC) in terms of efficiency, effectiveness, and scalability. According to our experiment results, biomedical ontology MeSH significantly enhances clustering quality on biomedical documents. In addition, our results show that decent document clustering approaches, such as Bisecting K-means, K-means and STC, gains some benefit from MeSH ontology while hierarchical algorithms showing the poorest clustering quality do not reap the benefit of MeSH ontology