Text Document Clustering Research Articles

Text document clustering using mayfly optimization algorithm with k-means technique

Text clustering is a subfield of machine learning (ML) and natural language processing (NLP) that consists of grouping similar sentences or documents based on their content. However, insignificant features in the documents minimize the accuracy of information retrieval which makes it challenging for the clustering approach to efficiently cluster similar documents. In this research, the mayfly optimization algorithm (MOA) with a k-means approach is proposed for text document clustering (TDC) to effectively cluster similar documents. Initially, the data is obtained from Reuters-21678, 20-Newsgroup, and BBC sports datasets, and then pre-processing is established by stemming and stop word removal to remove unwanted phrases or words. The data imbalance approach is established using an adaptive synthetic sampling algorithm (ADASYN), then term frequency-inverse document frequency (TD-IDF) and WordNet features are employed for extracting features. Finally, MOA with the K-means technique is utilized for TDC. The proposed approach achieves better accuracy of 99.75%, 99.54%, and 98.24% when compared to the existing techniques like fuzzy rough set-based robust nearest neighbor-convolutional neural network (FRS-RNN-CNN), TopicStriker, Modsup-based frequent itemset, and rider optimization-based moth search algorithm (Modsup-Rn-MSA), hierarchical dirichlet-multinomial mixture, and multi-view clustering via consistent and specific non-negative matrix (MCCS).

Text Document Clustering Using Chaotic Northern Goshawk Optimization with K-means Algorithm

Text Document Clustering Using Chaotic Northern Goshawk Optimization with K-means Algorithm

Improved Meta-Heuristic Model for Text Document Clustering by Adaptive Weighted Similarity

This paper intends to develop a novel framework for text document clustering with the aid of a new improved meta-heuristic algorithm. Initially, the features are selected from the text document by subjecting each word under Term Frequency-Inverse Document Frequency (TF-IDF) computation. Subsequently, centroid selection plays a vital role in cluster formation, which is done using a new Improved Lion Algorithm (LA) termed as Cross over probability-based LA model (CP-LA). As a novelty, this paper introduced a new inter and intracluster similarity model. Moreover, this centroid selection is made in such a way that the proposed adaptive weighted similarity should be minimal. Based on the characteristics of the document, the weights are automatically adapted with the similarity measure. The proposed adaptive weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents. Finally, the superiority of the proposed over other models is proved under different performance measures.

Text Document Clustering Approach by Improved Sine Cosine Algorithm

Due to the vast amounts of textual data available in various forms such as online content, social media comments, corporate data, public e-services and media data, text clustering has been experiencing rapid development. Text clustering involves categorizing and grouping similar content. It is a process of identifying significant patterns from unstructured textual data. Algorithms are being developed globally to extract useful and relevant information from large amounts of text data. Measuring the significance of content in documents to partition the collection of text data is one of the most important obstacles in text clustering. This study suggests utilizing an improved metaheuristics algorithm to fine-tune the K-means approach for text clustering task. The suggested technique is evaluated using the first 30 unconstrained test functions from the CEC2017 test-suite and six standard criterion text datasets. The simulation results and comparison with existing techniques demonstrate the robustness and supremacy of the suggested method.

An Enhanced Expectation Maximization Text Document Clustering Algorithm for E-Content Analysis

Nowadays, there are many types of digital materials that can be used in the classroom. Students and scholars are migrating from textbooks to digital study materials because textbooks are too large and expensive. Teachers and college students can use and modify the materials that are freely available or with some constraints for their learning and teaching. E-content can be designed, evolved, utilized, re-used, and distributed electronically from anywhere at anytime. Because of the flexibility of time, place, and speed of learning, e-content is becoming extremely popular. It can be readily and instantly shared and communicated with an infinite number of clients all across the globe. Document clustering is most commonly used to group documents that are related to a specific topic. Text document clustering can be used to group a collection of documents regarding the information they include and to deliver search results when a user searches the internet. In this paper mainly focuses on text document clustering to cope with massive collection of E-Content documents. Enhanced Expectation Maximization Text Document Clustering (EEMTDC) clustering algorithm was proposed and compared with Expectation Maximization (EM) clustering, K-Means clustering, and Hierarchical clustering (HC) algorithms. The experiment shows that the performance of proposed EEMTDC algorithm produces greater clustering accuracy than existing clustering algorithms.

Dynamic Sub-Swarm Approach of PSO Algorithms for Text Document Clustering.

Text document clustering is one of the data mining techniques used in many real-world applications such as information retrieval from IoT Sensors data, duplicate content detection, and document organization. Swarm intelligence (SI) algorithms are suitable for solving complex text document clustering problems compared to traditional clustering algorithms. The previous studies show that in SI algorithms, particle swarm optimization (PSO) provides an effective solution to text document clustering problems. This PSO still needs to be improved to avoid the problems such as premature convergence to local optima. In this paper, an approach called dynamic sub-swarm of PSO (subswarm-PSO) is proposed to improve the results of PSO for text document clustering problems and avoid the local optimum by improving the global search capabilities of PSO. The results of this proposed approach were compared with the standard PSO algorithm and K-means algorithm. As for performance assurance, the evaluation metric purity is used with six benchmark data sets. The experimental results of this study show that our proposed subswarm-PSO algorithm performs best with high purity comparing the standard PSO and K-means traditional algorithms and also the execution time of subswarm-PSO comparatively takes a little less than the standard PSO algorithm.

AN IMPROVED MULTI-VERSE OPTIMIZER FOR TEXT DOCUMENTS CLUSTERING

Text document clustering (TDC) represents a key task in text mining and unsupervised machine learning, which partitions a specific documents’ collection into varied K-groups according to certain similarity/dissimilarity criterion. The multi-verse optimizer algorithm (MVO) is a stochastic population-based algorithm, which was recently introduced and successfully utilized to tackle many optimization problems that are complex. The original MVO performance is limited to the utilization of only the best solution in the exploitation phase (local search capability), which makes it suffer from entrapment in local optima and low convergence rate. This paper aims to propose a novel method of modifying the MVO algorithm called link-based multi-verse optimizer algorithm (LBMVO) to enhance the exploitation phase in the original MVO. Generally, LBMVO has outperformed or at least showed that it is profoundly competitive compared with the original MVO algorithm.

Optimal Text Document Clustering Enabled by Weighed Similarity Oriented Jaya With Grey Wolf Optimization Algorithm

Abstract Owing to scientific development, a variety of challenges present in the field of information retrieval. These challenges are because of the increased usage of large volumes of data. These huge amounts of data are presented from large-scale distributed networks. Centralization of these data to carry out analysis is tricky. There exists a requirement for novel text document clustering algorithms, which overcomes challenges in clustering. The two most important challenges in clustering are clustering accuracy and quality. For this reason, this paper intends to present an ideal clustering model for text document using term frequency–inverse document frequency, which is considered as feature sets. Here, the initial centroid selection is much concentrated which can automatically cluster the text using weighted similarity measure in the proposed clustering process. In fact, the weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents, which is used to minimize weighted similarity among the documents. An advanced model for clustering is proposed by the hybrid optimization algorithm, which is the combination of the Jaya Algorithm (JA) and Grey Wolf Algorithm (GWO), and so the proposed algorithm is termed as JA-based GWO. Finally, the performance of the proposed model is verified through a comparative analysis with the state-of-the-art models. The performance analysis exhibits that the proposed model is 96.56% better than genetic algorithm, 99.46% better than particle swarm optimization, 97.09% superior to Dragonfly algorithm, and 96.21% better than JA for the similarity index. Therefore, the proposed model has confirmed its efficiency through valuable analysis.

A hybrid approach for text document clustering using Jaya optimization algorithm

In this digital era, millions of Internet users are contributing vast amounts of data in the form of unstructured text documents. Organizing this material is a tedious task. The clustering of text document plays a vital role for organizing these unstructured text documents. In our paper, we make use of Hybrid Jaya Optimization algorithm (HJO) for text Document Clustering (DC), referred to as HJO-DC. We have used the Silhouette index as a metric to measure the quality of a solution. The proposed work is compared with partitioning techniques such as K-Means and K-Medoids and metaheuristic techniques such as Genetic algorithm, Cuckoo Search, Particle Swarm Optimizer, Firefly and Grey Wolf Optimizer. Remarkably, the proposed algorithm achieves the highest quality clustering in all benchmark examples.

A novel ensemble statistical topic extraction method for scientific publications based on optimization clustering

The automatic topic extraction (TE) from scientific publications provides a very compact summary of the clusters’ contents. This often helps in locating information easily. TE enables us to define the boundaries of the scientific fields. Text Document Clustering (TDC) represents, in general, the first step of topic identification to identify the documents, which address a related subject matter. Metaheuristics are typically used as efficient approaches for TDC. The multi-verse optimizer algorithm (MVO) involves a stochastic population-based algorithm. It has been recently proposed and successfully utilized to tackle many hard optimization problems. In the TE process, the focus of each statistical TE method is placed on various language feature space aspects. The aim of this paper is to design a novel ensemble method for an automatic TE from a collection of scientific publications based on MVO as the clustering algorithm. The automatic TE, which is used in our approach, is term frequency-inverse document frequency (TF-IDF), most frequent based keyword extraction (TF), co-occurrence statistical information-based keyword extraction (CSI), TextRank (TR), and mutual information (MI). A group of candidate topics can be provided by each automatic TE method for the proposed ensemble method. Next, the ensemble approach prunes the candidate topics’ set via the application of a specific filtering heuristic. Then, their scores are recalculated based on the prescribed metrics. After that, for selecting a set of topics for certain scientific publications, dynamic threshold functions are applied. The findings emphasized the refined candidate set’s efficiency, as well as effectiveness. The results also showed that the system’s quality has been improved by new topics. The proposed method achieved better precision, as well as recall on a similar dataset compared to the state-of-the-art TE methods.

An ensemble topic extraction approach based on optimization clusters using hybrid multi-verse optimizer for scientific publications

For text document clustering (TDC), a novel hybrid of the multi-verse optimizer (MVO) algorithm and k-means (also called H-MVO) are proposed in this work. Moreover, a new ensemble method for an automatic topic extraction (TE) has been proposed in this paper, from a set of scientific publications in the form of text documents with the purpose of extracting topics from clustered documents. Often, the existing TE methods draw upon the statistical theory. However, the results might be different when the same clustered document is utilized. Consequently, there can be imprecise results, which are related to the extracted topics from the clustered documents owing to the behavior of the TE methods. As a result, the vigorous characteristics of the TE methods are ensembled, thereby empowering the accuracy of the extracted topics. The results, which were yielded by H-MVO for TDC, were compared against 14 well-regarded methods, involving five clustering methods, in addition to seven metaheuristic algorithms, as well as two hybrid optimization algorithms. Also, the results, which were generated by the introduced ensembled TE method, were compared against those, which were produced by five established statistical methods in the literature. As a result, the findings revealed that the suggested ensembled TE method outperformed the entire comparative methods, thereby utilizing all the external measurements for almost the entire datasets. Moreover, the new method can complement the advantages of the five previously proposed methods. Accordingly, more advanced results were obtained.

An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem.

Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. The performance of the text clustering is useful by adding the β operator to the hill climbing.

Combining Distributed Word Representation and Document Distance for Short Text Document Clustering

This paper presents a method for clustering short text documents, such as news headlines, social media statuses, or instant messages. Due to the characteristics of these documents, which are usually short and sparse, an appropriate technique is required to discover hidden knowledge. The objective of this paper is to identify the combination of document representation, document distance, and document clustering that yields the best clustering quality. Document representations are expanded by external knowledge sources represented by a Distributed Representation. To cluster documents, a K-means partitioning-based clustering technique is applied, where the similarities of documents are measured by word mover’s distance. To validate the effectiveness of the proposed method, experiments were conducted to compare the clustering quality against several leading methods. The proposed method produced clusters of documents that resulted in higher precision, recall, F1- score, and adjusted Rand index for both real-world and standard data sets. Furthermore, manual inspection of the clustering results was conducted to observe the efficacy of the proposed method. The topics of each document cluster are undoubtedly reflected by members in the cluster.

Link-based multi-verse optimizer for text documents clustering

Text document clustering (TDC) represents a key task in text mining and unsupervised machine learning, which partitions a specific documents’ collection into varied K-groups according to certain similarity/dissimilarity criterion. There exists a considerable amount of knowledge in the text clustering field and many attempts were carried out to resolve the TDC problem and improve the learning performance. The multi-verse optimizer algorithm (MVO) is a stochastic population-based algorithm, which was recently introduced and successfully utilized to tackle many optimization problems that are complex. The original MVO performance is limited to the utilization of only the best solution in the exploitation phase (local search capability), which makes it suffer from entrapment in local optima and low convergence rate. This paper aims to propose a novel method of modifying the MVO algorithm called link-based Multi-verse optimizer algorithm (LBMVO) to enhance the exploitation phase in the original MVO. The enhancement involves adding a neighbor operator to the MVO algorithm to enhance the search capability via a novel probability factor, namely neighborhood selection strategy (NSS). The proposed LBMVO’s effectiveness was tested on six standard datasets, which are used in the text clustering domain in addition to five standard datasets, which are utilized in the data clustering domain. The experiments revealed that the modified MVO with NSS has boosted the results in terms of error rate, accuracy, recall, precision, F-measure, purity, entropy criteria, and high convergence rate. Generally, LBMVO has outperformed or at least showed that it is profoundly competitive compared with the original MVO algorithm and with widely known clustering techniques like Spectral, Agglomerative, Density-based spatial clustering of applications with noise (DBSCAN), K-means, K-means++ clustering techniques and the optimization algorithms like harmony search (HS), genetic algorithm (GA), particle swarm optimization (PSO), krill herd algorithm (KHA), covariance matrix adaptation evolution strategy (CMAES), coyote optimization algorithm (COA), as well as original MVO.

Hybridization of a Social Spider Optimization Algorithm with Differential Evolution for Text Document Clustering Using Single Cluster Approach

Hybridization of a Social Spider Optimization Algorithm with Differential Evolution for Text Document Clustering Using Single Cluster Approach

Text-document clustering-based cause and effect analysis methodology for steel plant incident data

ABSTRACTThe purpose of this study is to develop a text clustering-based cause and effect analysis methodology for incident data to unfold the root causes behind the incidents. A cause–effect diagram is usually prepared by using experts’ knowledge which may fail to capture all the causes present at a workplace. On the other hand, the description of incidents provided by the workers in the form of incident reports is typically a rich data source and can be utilized to explore the causes and sub-causes of incidents. In this study, data were collected from an integrated steel plant. The text data were analysed using singular value decomposition (SVD) and expectation-maximization (EM) algorithm. Results suggest that text-document clustering can be used as a feasible method for exploring the hidden factors and trends from the description of incidents occurred at workplaces. The study also helped in finding out the anomaly in incident reporting.

A Survey on Optimization Approaches to Text Document Clustering

Text Document Clustering is one of the fastest growing research areas because of availability of huge amount of information in an electronic form. There are several number of techniques launched for clustering documents in such a way that documents within a cluster have high intra-similarity and low inter-similarity to other clusters. Many document clustering algorithms provide localized search in effectively navigating, summarizing, and organizing information. A global optimal solution can be obtained by applying high-speed and high-quality optimization algorithms. The optimization technique performs a globalized search in the entire solution space. In this paper, a brief survey on optimization approaches to text document clustering is turned out.

Ontology Employment in Text Document Clustering combined with Grouping Algorithm

Ontology Employment in Text Document Clustering combined with Grouping Algorithm

Evaluation of text document clustering approach based on particle swarm optimization

Abstract Clustering, an extremely important technique in Data Mining is an automatic learning technique aimed at grouping a set of objects into subsets or clusters. The goal is to create clusters that are coherent internally, but substantially different from each other. Text Document Clustering refers to the clustering of related text documents into groups based upon their content. It is a fundamental operation used in unsupervised document organization, text data mining, automatic topic extraction, and information retrieval. Fast and high-quality document clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. The documents to be clustered can be web news articles, abstracts of research papers etc. This paper proposes two techniques for efficient document clustering involving the application of soft computing approach as an intelligent hybrid approach PSO algorithm. The proposed approach involves partitioning Fuzzy C-Means algorithm and K-Means algorithm each hybridized with Particle Swarm Optimization (PSO). The performance of these hybrid algorithms has been evaluated against traditional partitioning techniques (K-Means and Fuzzy C Means).

A Fuzzy Based Approach to Text Mining and Document Clustering

Fuzzy logic deals with degrees of truth. In this paper, we have shown how to apply fuzzy logic in text mining in order to perform document clustering. We took an example of document clustering where the documents had to be clustered into two categories. The method involved cleaning up the text and stemming of words. Then, we chose ‘m’ features which differ significantly in their word frequencies (WF), normalized by document length, between documents belonging to these two clusters. The documents to be clustered were represented as a collection of ‘m’ normalized WF values. Fuzzy c-means (FCM) algorithm was used to cluster these documents into two clusters. After the FCM execution finished, the documents in the two clusters were analysed for the values of their respective ‘m’ features. It was known that documents belonging to a document type ‘X’ tend to have higher WF values for some particular features. If the documents belonging to a cluster had higher WF values for those same features, then that cluster was said to represent ‘X’. By fuzzy logic, we not only get the cluster name, but also the degree to which a document belongs to a cluster.