Document Clustering Techniques Research Articles

Document Clustering in the Age of Big Data: Incorporating Semantic Information for Improved Results

There has been a meteoric rise in the total amount of digital texts as a direct result of the proliferation of internet access. As a direct result of this, document clustering has evolved into a crucial method that must be used in order to successfully extract relevant information from big document collections. When employing the document clustering approach, documents are automatically sorted into groups whose members have a high degree of similarity to one another. These groups are created by applying the document clustering technique. Because they do not take into account the semantic linkages that exist between the texts, traditional clustering approaches are unable to provide an acceptable description of a collection of texts. This is because traditional clustering techniques. Document clusters, in which texts are ordered according to their meaning rather than their use of keywords, have been extensively utilized as a means of overcoming these challenges as a result of the incorporation of semantic information. This has been possible as a result of the fact that document clusters can group together related texts. In this investigation, we looked at a total of 27 distinct papers that were published over the previous five years and categorized the documents based on the semantic similarities that existed between the various pieces. A detailed literature evaluation is included to each and every one of the publications that were selected for further consideration. Comparative research is carried out on a wide variety of evaluation strategies, including as algorithms, similarity metrics, instruments, and processes. Following that, there is a drawn-out discussion that analyzes the similarities and differences between the activities.

Identification and Visualization of Key Topics in Scientific Publications with Transformer-Based Language Models and Document Clustering Methods

With the rapidly growing number of scientific publications, researchers face an increasing challenge of discovering the current research topics and methodologies in a scientific domain. This paper describes an unsupervised topic detection approach that utilizes the new development of transformer-based GPT-3 (Generative Pretrained Transformer 3) similarity embedding models and modern document clustering techniques. In total, 593 publication abstracts across urban study and machine learning domains were used as a case study to demonstrate the three phases of our approach. The iterative clustering phase uses the GPT-3 embeddings to represent the semantic meaning of abstracts and deploys the HDBSCAN (Hierarchical Density-based Spatial Clustering of Applications with Noise) clustering algorithm along with silhouette scores to group similar abstracts. The keyword extraction phase identifies candidate words from each abstract and selects keywords using the Maximal Marginal Relevance ranking algorithm. The keyword grouping phase produces the keyword groups to represent topics in each abstract cluster, again using GPT-3 embeddings, the HDBSCAN algorithm, and silhouette scores. The results are visualized in a web-based interactive tool that allows users to explore abstract clusters and examine the topics in each cluster through keyword grouping. Our unsupervised topic detection approach does not require labeled datasets for training and has the potential to be used in bibliometric analysis in a large collection of publications.

Construction of suffix tree using key phrases for document using down-top incremental conceptual hierarchical text clustering approach

With development of technologies in the World Wide Web, usage of document increases day by day. In order to access the document easily, document clustering technique is introduced. In the field of data mining, document clustering plays a vital role. Organizing the unstructured and unlabeled document is one of the major problems and it is ever growing and complex. Handling of such unorganized documents causes more expensive. Hence, challenges raised by the continuing growth of unstructured and unlabeled documents are handled in this proposed work. Document clustering is one of the most powerful methods to solve the problem of organizing unstructured documents. There are numerous clustering methods available. In this we were proposed phrase-based clustering algorithm, which is based on the applications of suffix tree document clustering model. The proposed algorithm is designed to use the suffix tree document clustering (STDC) model for accurate representation of document and similarity measurement of the similar documents. This proposed algorithm gives more than 90% accuracy.

Comparing the Performance of SOM with Traditional Methods for Document Clustering Using Wordnet Ontologies

Abstract: Retrieving useful information has become challenging due to the rapid expansion of web material. To improve the retrieval outcomes, efficient clustering methods are required. Document clustering is the process of identifying similarities and differences among given objects and grouping them into clusters with comparable features. We used WordNet lexical as an addition to compare several document clustering techniques in this article. The suggested method employs WordNet to determine the relevance of the concepts in the text, and then clusters the content using several document clustering algorithms (K-means, Agglomerative Clustering, and self-organizing maps). We wish to compare alternative ways for making document clustering algorithms more successful. Keywords: Document clustering, Clustering technique, Self-organizing maps, WordNet, K-means, Hierarchical Clustering.

Automatic Labeling of Clusters for a Low-Resource Urdu Language

Document clustering techniques often produce clusters that require human intervention to interpret the meaning of such clusters. Automatic cluster labeling refers to the process of assigning a meaningful phrase to a cluster as a label. This article proposes an unsupervised method for cluster labeling that is based on noun phrase chunking. The proposed method is compared with four other statistical-based methods, including Z-Order, M-Order, T-Order, and YAKE. In addition to the statistical measures based labeling schemes, the approach is also compared with two graph-based techniques: TextRank and PositionRank. The experiments were performed on the low-resource Urdu language corpus of News Headlines. The proposed approach's effectiveness was evaluated using cosine similarity, the Jaccard index, and feedback received from human evaluators. The results show that the proposed method outperforms other methods. It was found that the labels produced were more relevant and semantically rich in contrast to other approaches.

Analisis Dan Implementasi Algoritma Active Fuzzy Constrained Clustering Untuk Pengelompokan Dokumen

Text document clustering techniques automatically become an important research in which volume of text document via digital media is growing rapidly. This technique is known as document clustering. Document clustering is a method of grouping documents based on their similarity. For groupping these documents, one of the clustering algorithms is used, namely Active Fuzzy Constrained Clustering (AFCC), which combines fuzzy and semi-supervised clustering methods where text documents as a bag of words will be calculated with the value of meaningful words using the Vector Space Model. (VSM). The AFCC algorithm is identified by the use of pairwise constraint and centroid in its cluster. The input documents tested in the research are a collection of documents in the BBC News Archives. Based on the research that has been done, using the parameters of the maximum number of clusters, the maximum number of constraints per iteration and the maximum number of iterations, the AFCC algorithm results in grouping text documents that are news article. Performance measurement of clustering results in this research uses the Confusion Matrix approach, which can be generated with an average precision and recall value of 0.53, and an accuracy value of 0.52

Application of Convolution Neural Networks in Web Search Log Mining for Effective Web Document Clustering

The volume of web search data stored in search engine log is increasing and has become big search log data. The web search log has been the source of data for mining based on web document clustering techniques to improve the efficiency and effectiveness of information retrieval. In this paper Deep Learning Model Convolution Neural Network(CNN) is used in big web search log data mining to learn the semantic representation of a document. These semantic documents vectors are clustered using K-means to group relevant documents for effective web document clustering. Experiment was done on the data set of web search query and associated clicked URLs to measure the quality of clusters based on document semantic representation using Deep learning model CNN. The clusters analysis was performed based on WCSS(the sum of squared distances of documents samples to their closest cluster center) and decrease in the WCSS in comparison to TF.IDF keyword based clusters confirm the effectiveness of CNN in web search log mining for effective web document clustering.

Automatic Text Summarization using Document Clustering Named Entity Recognition

Due to the rapid development of internet technology, social media and popular research article databases have generated many open text information. This large amount of textual information leads to 'Big Data'. Textual information can be recorded repeatedly about an event or topic on different websites. Text summarization (TS) is an emerging research field that helps to produce summary from a single or multiple documents. The redundant information in the documents is difficult, hence part or all of the sentences may be omitted without changing the gist of the document. TS can be organized as an exposition to collect accents from its special position, rather than being semantic in nature. Non-ASCII characters and pronunciation, including tokenizing and lemmatization are involved in generating a summary. This research work has proposed an Entity Aware Text Summarization using Document Clustering (EASDC) technique to extract summary from multi-documents. Named Entity Recognition (NER) has a vital part in the proposed work. The topics and key terms are identified using the NER technique. Extracted entities are ranked with Zipf’s law and sentence clusters are formed using k-means clustering. Cosine similarity-based technique is used to eliminate the similar sentences from multi-documents and produce unique summary. The proposed EASDC technique is evaluated using CNN dataset and it shown an improvement of 1.6 percentage when compared with the baseline methods of Textrank and Lexrank.

Knowledge tracing: A bibliometric analysis

With the advent of artificial intelligence, most of the main techniques have found their way into intelligent education. Knowledge tracing is one of the essential tasks in educational research, which aims to model and qualify students' procedural knowledge acquisition using machine learning or deep learning techniques. While numerous studies have focused on improving models and algorithms of knowledge tracing, few have thoroughly examined the dynamic and complex aspects of this research field. This study conducts a bibliometric analysis that included 383 key articles published between 1992 and 2021 to review the evolutionary nuances of knowledge tracing research. Besides, we employ document clustering to uncover the most common topics of knowledge tracing and systematically review each topic's characteristics. Major findings include broad knowledge tracing trends information such as the most productive authors, the most referenced articles, and the occurrence of author keywords. Existing knowledge tracing models are further divided into three clusters: Markov process-based knowledge tracing, logistic knowledge tracing, and deep learning-based knowledge tracing. The attributes of each cluster were then discussed, as well as recent development and application. Finally, we highlighted existing constraints and identified promising future research topics in knowledge tracing. • A bibliometric analysis was performed to reveal knowledge tracing research trends. • Using document clustering techniques to divide the existing knowledge tracing research into three topics. • Investigating the attributes and recent development of knowledge tracing models. • Summarizing the popular applications of knowledge tracing models.

Retracted] Big Data Analytics Model for Distributed Document Using Hybrid Optimization with K‐Means Clustering

Clustering, also known as unsupervised learning, is one of the most significant topics of machine learning because it divides data into groups based on similarity with the aim of extracting or summarizing new information. It is one of the most often used machine learning techniques. The most significant problem encountered in this subject is the sheer volume of electronic text documents accessible, which is increasing at an exponential rate, necessitating the development of efficient ways for dealing with these papers. Furthermore, it is not practicable to consolidate all of the papers from numerous locations into a single area for processing. In this study, the primary goal is to enhance the performance of the distributed document clustering approach for clustering big, high‐dimensional distributed document datasets. For distributed storage and analysis, one of the most prominent open‐source implementations of the big data analytic‐based MapReduce model, such as the Hadoop framework, is used in conjunction with a distributed file system and is known as the Hadoop Distributed File System, to achieve the desired results. This necessitates an improvement in the approach of the clustering operation with Elephant Herding Optimization, which will be accomplished by applying a hybridized clustering procedure. In conjunction with the MapReduce framework, this hybridized strategy is able to solve the obstacles associated with the K‐means clustering method, including the initial centroids difficulty and the dimensionality problem. In this paper, we analyze the performance of the whole distributed document clustering technique for big document datasets by using a distributed document clustering framework such as Hadoop and the associated MapReduce methodology. In the end, this decides how quickly computations may be completed.

Stamantic clustering: Combining statistical and semantic features for clustering of large text datasets

Document clustering in text mining is a problem that is heavily researched upon. It is observed that individual approaches based on statistical features and semantic features have been extensively used to solve this problem. However, techniques combining the advantages of both types of features have not been frequently researched upon. Specifically, when the growth in the size of textual data is immense, there is a need for such an approach that combines the advantages of both types of features to give more accurate results within an acceptable range of time. In this paper, a document clustering technique is proposed that combines the effectiveness of the statistical features (using TF-IDF) and semantic features (using lexical chains). It is designed to use a fewer number of features while maintaining a comparable and even better accuracy for the task of document clustering.

Unsupervised neural networks for automatic Arabic text summarization using document clustering and topic modeling

Humans must easily handle the vast amounts of data being generated by the revolution of information technology. Thus, Automatic Text summarization has been applied to various domains in order to find the most relevant information and make critical decisions quickly. In the context of Arabic, text summarization techniques suffer from several problems. First, most existing methods do not consider the context or domain to which the document belongs. Second, the majority of the existing approaches are based on the traditional bag-of-words representation, which involves high dimensional and sparse data, and makes it difficult to capture relevant information. Third, research in Arabic Text summarization is fairly small and only recently compared to that on Anglo-Saxon and other languages due to the shortage of Arabic corpora, resources, and automatic processing tools. In this paper, we try to overcome these limitations by proposing a new approach using documents clustering, topic modeling, and unsupervised neural networks in order to build an efficient document representation model. First, a new document clustering technique using Extreme learning machine is performed on large text collection. Second, topic modeling is applied to documents collection in order to identify topics present in each cluster. Third, each document is represented in a topic space by a matrix where rows represent the document sentences and columns represent the cluster topics. The generated matrix is then trained using several unsupervised neural networks and ensemble learning algorithms in order to build an abstract representation of the document in the concept space. Important sentences are ranked and extracted according to a graph model with a redundancy elimination component. The proposed approach is evaluated on Essex Arabic Summaries Corpus and compared against other Arabic text summarization approaches using ROUGE measure. Experimental results showed that the models trained on topic representation learn better representations and improve significantly the summarization performance. In particular, ensemble learning models demonstrated an important improvement on Rouge recall and promising results on F-measure.

Semantics-based clustering approach for similar research area detection

The manual process of searching out individuals in an already existing research field is cumbersome and time-consuming. Prominent and rookie researchers alike are predisposed to seek existing research publications in a research field of interest before coming up with a thesis. From extant literature, automated similar research area detection systems have been developed to solve this problem. However, most of them use keyword-matching techniques, which do not sufficiently capture the implicit semantics of keywords thereby leaving out some research articles. In this study, we propose the use of Ontology-based pre-processing, Latent Semantic Indexing and K-Means Clustering to develop a prototype similar research area detection system, that can be used to determine similar research domain publications. Our proposed system solves the challenge of high dimensionality and data sparsity faced by the traditional document clustering technique. Our system is evaluated with randomly selected publications from faculties in Nigerian universities and results show that the integration of ontologies in preprocessing provides more accurate clustering results.

Role of Pre-processing Phase in Document Clustering Technique for Gurmukhi Script

Document clustering plays a central role in knowledge discovery and data mining by representing large data-sets into a certain number of data objects called clusters. Each cluster consists similar data objects in such a way that data objects in the same cluster are more similar and dissimilar to the data objects of other clusters. Document clustering technique for Gurmukhi script consists two phases namely: 1) Pre-processing phase 2) Processing phase. This paper concentrates pre-processing phase of document clustering technique for Gurmukhi script. The purpose of pre-processing phase is to convert unstructured text into structured text format. Various sub-phases of pre-processing phase are: segmentation, tokenization, removal of stop words, stemming, and normalization. The purpose of this paper is to present the significant role of pre-processing phase in an overall performance of document clustering technique for Gurmukhi script. The experimental results represent the significant role of pre-processing phase in terms of performance regarding assignment of data objects to the relevant clusters as well as in creation of meaningful cluster title list.

Performance Exploration on Various Document Clustering Techniques with K-Means Family

Clustering performs a important position in numerous fields which include Computer science & packages, facts, pattern reputation, system studying technique and find out dating among the files. Clustering focuses on document clustering, and other related area. Increase within the extent of statistics saved in virtual form (text, photograph, audio) has improved the need for requirement of an automated tool, that allows people to find and manage the records in an efficient way. Usually clustering refer to document clustering technique investigates the documents and find its relation. This paper center of attention on the a range of clustering methods and evaluation its overall performance. This paper also categories the document clustering techniques as three major groups, namely Group K-means, Expectation Maximization and Semantic-based techniques (Hybrid method). Several experiments were conducted to analyse the performance accuracy and Speed.

Development of Document Clustering Technique for Gurmukhi Script using Fuzzy Term Weight

Document clustering is an unsupervised machine learning technique which designates the creation of classes of a certain number of similar objects without prior knowledge of data-sets. These classes of similar objects are known as clusters; each cluster consists unlabeled data objects in such a way that data objects within the same cluster have maximum similarity and have dissimilarity to the data objects of other groups. The purpose of this research work is to develop domain independent Gurmukhi script clustering technique. It is the first ever effort as no prior work has been done to develop domain independent clustering technique for Gurmukhi script. In this paper, a hybrid algorithm for the development of document clustering technique for Gurmukhi script has been developed. The experimental results of proposed document clustering technique reveal that the proposed hybrid technique performs better in terms of defining number of clusters, creation of meaningful cluster titles, and in terms of performance regarding assignment of real time unlabeled data sets to the relevant cluster as a result of various pre-processing steps like segmentation, stemming, normalization as well as extraction of named/noun entities, creation of cluster titles and placing text documents into relevant clusters using fuzzy term weight.

Critical Analysis of Clinical Document Clustering Technique with Special Reference to Non-Matrix Factorization

Critical Analysis of Clinical Document Clustering Technique with Special Reference to Non-Matrix Factorization

Automatic Scientific Document Clustering Using Self-organized Multi-objective Differential Evolution

Document clustering is the partitioning of a given collection of documents into various K- groups based on some similarity/dissimilarity criterion. This task has applications in scope detection of journals/conferences, development of some automated peer-review support systems, topic-modeling, latest cognitive-inspired works on text summarization, and classification of documents based on semantics, etc. In the current paper, a cognitive-inspired multi-objective automatic document clustering technique is proposed which is a fusion of self-organizing map (SOM) and multi-objective differential evolution approach. The variable number of cluster centers are encoded in different solutions of the population to determine the number of clusters from a data set in an automated way. These solutions undergo various genetic operations during evolution. The concept of SOM is utilized in designing new genetic operators for the proposed clustering technique. In order to measure the goodness of a clustering solution, two cluster validity indices, Pakhira-Bandyopadhyay-Maulik index, and Silhouette index, are optimized simultaneously. The effectiveness of the proposed approach, namely self-organizing map based multi-objective document clustering technique (SMODoc_clust) is shown in automatic classification of some scientific articles and web-documents. Different representation schemas including tf, tf-idf and word-embedding are employed to convert articles in vector-forms. Comparative results with respect to internal cluster validity indices, namely, Dunn index and Davies-Bouldin index, are shown against several state-of-the-art clustering techniques including three multi-objective clustering techniques namely MOCK, VAMOSA, NSGA-II-Clust, single objective genetic algorithm (SOGA) based clustering technique, K-means, and single-linkage clustering. Results obtained clearly show that our approach is better than existing approaches. The validation of the obtained results is also shown using statistical significant t tests.

Embedded Fuzzy Bilingual Dictionary model for cross language information retrieval systems

The goal of Cross language information Retrieval (CLIR) is the mapping of query written in one language (source language) to document collection in another language (target language). Over the years, CLIR employs approaches like dictionary translation, machine translations and query expansion to output documents based on users query. While relevance of retrieved document ranks high for the success of any Information Retrieval operation, obtaining relevance is encumbered with several issues compounded by the methods employed in CLIR. Thus, the research examines some of these existing methods in CLIR towards extending the dictionary translator and query expansion model. Here, a Fuzzy Bilingual Dictionary (FBD) with a dual concept driven document-clustering technique was proposed with a bid to retrieve more relevant document across language. The resulting model was tested on 3000 dataset of computer science related articles with queries posed in Yoruba language. A better recall and precision rate of the retrieval operation was achieved from the experiments.

Clustering News Articles using Efficient Similarity Measure and N-grams

The rapid progress of information technology and web makes it easier to store huge amount of collected textual information, e.g., blogs, news articles, e-mail messages, reviews and forum postings. The growing size of textual dataset with high-dimensions and natural language pose a big challenge making it hard for such information to be categorised efficiently. Document clustering is an automatic unsupervised machine learning technique that aimed at grouping related set of items into clusters or subsets. The target is creating clusters with high internal coherence, but different from each other substantially. This paper presents a new document clustering technique using N-grams and efficient similarity measure known as 'improved sqrt-cosine similarity measure'. Comprehensive experiments are conducted to evaluate our proposed clustering technique and compared with an existing method. The results of the experiments show that our proposed clustering technique outperforms the existing techniques.