PDC-Transitive: An Enhanced Heuristic for Document Clustering Based on Relational Analysis Approach and Iterative MapReduce

2018 â—½  
Vol 17 (02) â—½  
pp. 1850021
Author(s):  
Yasmine Lamari â—½  
Said Chah Slaoui
Keyword(s):  
Clustering Algorithms â—½  
Computing Time â—½  
Document Clustering â—½  
Large Data â—½  
Original Method â—½  
Data Partitioning â—½  
Data Dependencies â—½  
Clustering Problem â—½  
Relational Analysis â—½  
Benchmark Datasets

Recently, MapReduce-based implementations of clustering algorithms have been developed to cope with the Big Data phenomenon, and they show promising results particularly for the document clustering problem. In this paper, we extend an efficient data partitioning method based on the relational analysis (RA) approach and applied to the document clustering problem, called PDC-Transitive. The introduced heuristic is parallelised using the MapReduce model iteratively and designed with a single reducer which represents a bottleneck when processing large data, we improved the design of the PDC-Transitive method to avoid the data dependencies and reduce the computation cost. Experiment results on benchmark datasets demonstrate that the enhanced heuristic yields better quality results and requires less computing time compared to the original method.

A Brief Review of Metaheuristics for Document or Text Clustering

2016 â—½  
pp. 252-264 â—½  
Author(s):  
Sinem Büyüksaatçı â—½  
Alp Baray
Keyword(s):  
Language Processing â—½  
Clustering Algorithms â—½  
Document Clustering â—½  
Text Clustering â—½  
Metaheuristic Algorithms â—½  
Research Papers â—½  
High Quality â—½  
Topic Extraction â—½  
Clustering Problem â—½  
Research Areas

Document clustering, which involves concepts from the fields of information retrieval, automatic topic extraction, natural language processing, and machine learning, is one of the most popular research areas in data mining. Due to the large amount of information in electronic form, fast and high-quality cluster analysis plays an important role in helping users to effectively navigate, summarize and organise this information for useful data. There are a number of techniques in the literature, which efficiently provide solutions for document clustering. However, during the last decade, researchers started to use metaheuristic algorithms for the document clustering problem because of the limitations of the existing traditional clustering algorithms. In this chapter, the authors will give a brief review of various research papers that present the area of document or text clustering approaches with different metaheuristic algorithms.

Hierarchical Document Clustering

2011 â—½  
pp. 970-975 â—½  
Author(s):  
Benjamin C.M. Fung â—½  
Ke Wang â—½  
Martin Ester
Keyword(s):  
Hierarchical Clustering â—½  
Clustering Algorithms â—½  
Document Clustering â—½  
High Volume â—½  
Frequent Itemset â—½  
Parent Child Relationship â—½  
Text Documents â—½  
Clustering Problem â—½  
Child Relationship â—½  
Divisive Hierarchical Clustering

Document clustering is an automatic grouping of text documents into clusters so that documents within a cluster have high similarity in comparison to one another, but are dissimilar to documents in other clusters. Unlike document classification (Wang, Zhou, & He, 2001), no labeled documents are provided in clustering; hence, clustering is also known as unsupervised learning. Hierarchical document clustering organizes clusters into a tree or a hierarchy that facilitates browsing. The parent-child relationship among the nodes in the tree can be viewed as a topic-subtopic relationship in a subject hierarchy such as the Yahoo! directory. This chapter discusses several special challenges in hierarchical document clustering: high dimensionality, high volume of data, ease of browsing, and meaningful cluster labels. State-of-the-art document clustering algorithms are reviewed: the partitioning method (Steinbach, Karypis, & Kumar, 2000), agglomerative and divisive hierarchical clustering (Kaufman & Rousseeuw, 1990), and frequent itemset-based hierarchical clustering (Fung, Wang, & Ester, 2003). The last one, which was developed by the authors, is further elaborated since it has been specially designed to address the hierarchical document clustering problem.

Clustering techniques and their applications in engineering

2007 â—½  
Vol 221 (11) â—½  
pp. 1445-1459 â—½  
Author(s):  
D T Pham â—½  
A A Afify
Keyword(s):  
Data Mining â—½  
Clustering Algorithms â—½  
Large Data â—½  
Large Data Sets â—½  
Data Sets â—½  
Monitoring And Control â—½  
Design Quality â—½  
Clustering Problem â—½  
Manufacturing System Design â—½  
And Control

Clustering is an important data exploration technique with many applications in different areas of engineering, including engineering design, manufacturing system design, quality assurance, production planning and process planning, modelling, monitoring, and control. The clustering problem has been addressed by researchers from many disciplines. However, efforts to perform effective and efficient clustering on large data sets only started in recent years with the emergence of data mining. The current paper presents an overview of clustering algorithms from a data mining perspective. Attention is paid to techniques of scaling up these algorithms to handle large data sets. The paper also describes a number of engineering applications to illustrate the potential of clustering algorithms as a tool for handling complex real-world problems.

scholarly journals Max stable set problem to found the initial centroids in clustering problem

2022 â—½  
Vol 25 (1) â—½  
pp. 569
Author(s):  
Awatif Karim â—½  
Chakir Loqman â—½  
Youssef Hami â—½  
Jaouad Boumhidi
Keyword(s):  
Document Clustering â—½  
Large Data â—½  
Hopfield Network â—½  
Large Data Sets â—½  
Stable Set â—½  
Data Sets â—½  
Clustering Problem â—½  
Text Document â—½  
Stable Set Problem

In this paper, we propose a new approach to solve the document-clustering using the K-Means algorithm. The latter is sensitive to the random selection of the k cluster centroids in the initialization phase. To evaluate the quality of K-Means clustering we propose to model the text document clustering problem as the max stable set problem (MSSP) and use continuous Hopfield network to solve the MSSP problem to have initial centroids. The idea is inspired by the fact that MSSP and clustering share the same principle, MSSP consists to find the largest set of nodes completely disconnected in a graph, and in clustering, all objects are divided into disjoint clusters. Simulation results demonstrate that the proposed K-Means improved by MSSP (KM_MSSP) is efficient of large data sets, is much optimized in terms of time, and provides better quality of clustering than other methods.

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

2020 â—½  
Vol 16 (4) â—½  
pp. 296-306 â—½  
Author(s):  
Laith Mohammad Abualigah â—½  
Essam Said Hanandeh â—½  
Ahamad Tajudin Khader â—½  
Mohammed Abdallh Otair â—½  
Shishir Kumar Shandilya
Keyword(s):  
Optimization Technique â—½  
Document Clustering â—½  
Text Clustering â—½  
Hill Climbing â—½  
Text Documents â—½  
Clustering Problem â—½  
Text Document â—½  
Text Information â—½  
Amount Of Knowledge â—½  
The Hill

Background: 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. Aims: 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. Methods: 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. Results: 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. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

Using single-cell cytometry to illustrate integrated multi-perspective evaluation of clustering algorithms using Pareto fronts

Bioinformatics â—½  
2021 â—½  
Author(s):  
Givanna H Putri â—½  
Irena Koprinska â—½  
Thomas M Ashhurst â—½  
Nicholas J C King â—½  
Mark N Read
Keyword(s):  
Single Cell â—½  
Performance Metrics â—½  
Clustering Algorithms â—½  
Latin Hypercube Sampling â—½  
Supplementary Information â—½  
Sequencing Data â—½  
Evaluation Protocol â—½  
Benchmark Datasets â—½  
Pareto Fronts â—½  
Parameter Values

Abstract Motivation Many ‘automated gating’ algorithms now exist to cluster cytometry and single-cell sequencing data into discrete populations. Comparative algorithm evaluations on benchmark datasets rely either on a single performance metric, or a few metrics considered independently of one another. However, single metrics emphasize different aspects of clustering performance and do not rank clustering solutions in the same order. This underlies the lack of consensus between comparative studies regarding optimal clustering algorithms and undermines the translatability of results onto other non-benchmark datasets. Results We propose the Pareto fronts framework as an integrative evaluation protocol, wherein individual metrics are instead leveraged as complementary perspectives. Judged superior are algorithms that provide the best trade-off between the multiple metrics considered simultaneously. This yields a more comprehensive and complete view of clustering performance. Moreover, by broadly and systematically sampling algorithm parameter values using the Latin Hypercube sampling method, our evaluation protocol minimizes (un)fortunate parameter value selections as confounding factors. Furthermore, it reveals how meticulously each algorithm must be tuned in order to obtain good results, vital knowledge for users with novel data. We exemplify the protocol by conducting a comparative study between three clustering algorithms (ChronoClust, FlowSOM and Phenograph) using four common performance metrics applied across four cytometry benchmark datasets. To our knowledge, this is the first time Pareto fronts have been used to evaluate the performance of clustering algorithms in any application domain. Availability and implementation Implementation of our Pareto front methodology and all scripts and datasets to reproduce this article are available at https://github.com/ghar1821/ParetoBench. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Natural language processing methods for knowledge management—Applying document clustering for fast search and grouping of engineering documents

2021 â—½  
pp. 1063293X2098297
Author(s):  
Ivar Örn Arnarsson â—½  
Otto Frost â—½  
Emil Gustavsson â—½  
Mats Jirstrand â—½  
Johan Malmqvist
Keyword(s):  
Natural Language Processing â—½  
Natural Language â—½  
Language Processing â—½  
Domain Knowledge â—½  
Clustering Algorithms â—½  
Document Clustering â—½  
Unstructured Data â—½  
Free Text â—½  
Engineering Change â—½  
Engineering Documents

Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested.

Biomedical Document Clustering Based on Accelerated Symbiotic Organisms Search Algorithm

2021 â—½  
Vol 12 (4) â—½  
pp. 169-185
Author(s):  
Saida Ishak Boushaki â—½  
Omar Bendjeghaba â—½  
Nadjet Kamel
Keyword(s):  
Clustering Algorithm â—½  
Search Algorithm â—½  
Clustering Algorithms â—½  
Document Clustering â—½  
Latent Semantic Indexing â—½  
Research Area â—½  
Semantic Indexing â—½  
Local Optima â—½  
Symbiotic Organisms Search â—½  
Symbiotic Organisms

Clustering is an important unsupervised analysis technique for big data mining. It finds its application in several domains including biomedical documents of the MEDLINE database. Document clustering algorithms based on metaheuristics is an active research area. However, these algorithms suffer from the problems of getting trapped in local optima, need many parameters to adjust, and the documents should be indexed by a high dimensionality matrix using the traditional vector space model. In order to overcome these limitations, in this paper a new documents clustering algorithm (ASOS-LSI) with no parameters is proposed. It is based on the recent symbiotic organisms search metaheuristic (SOS) and enhanced by an acceleration technique. Furthermore, the documents are represented by semantic indexing based on the famous latent semantic indexing (LSI). Conducted experiments on well-known biomedical documents datasets show the significant superiority of ASOS-LSI over five famous algorithms in terms of compactness, f-measure, purity, misclassified documents, entropy, and runtime.

scholarly journals A semantic approach for text document clustering using frequent itemsets and WordNet

2018 â—½  
Vol 7 (2.18) â—½  
pp. 102
Author(s):  
Harsha Patil â—½  
Ramjeevan Singh Thakur
Keyword(s):  
Clustering Algorithms â—½  
Document Clustering â—½  
Knowledge Bases â—½  
Experimental Result â—½  
Semantic Approach â—½  
Text Document â—½  
Clustering Quality â—½  
Ship Function â—½  
Membership Score â—½  
Specific Cluster

Document Clustering is an unsupervised method for classified documents in clusters on the basis of their similarity. Any document get it place in any specific cluster, on the basis of membership score, which calculated through membership function. But many of the traditional clustering algorithms are generally based on only BOW (Bag of Words), which ignores the semantic similarity between document and Cluster. In this research we consider the semantic association between cluster and text document during the calculation of membership score of any document for any specific cluster. Several researchers are working on semantic aspects of document clustering to develop clustering performance. Many external knowledge bases like WordNet, Wikipedia, Lucene etc. are utilized for this purpose. The proposed approach exploits WordNet to improve cluster member ship function. The experimental result shows that clustering quality improved significantly by using proposed framework of semantic approach. 

scholarly journals Bpop: an efficient program for estimating base population allele frequencies in single and multiple group structured populations

2020 â—½  
Vol 29 (3) â—½  
Author(s):  
Ismo Stranden â—½  
Esa A. Mäntysaari
Keyword(s):  
Computing Time â—½  
Large Data â—½  
Generalized Least Squares â—½  
Structured Populations â—½  
Allele Frequencies â—½  
Data Sets â—½  
Dense Matrix â—½  
Base Population â—½  
Multiple Group â—½  
Efficient Program

Base population allele frequencies (AF) should be used in genomic evaluations. A program named Bpop was implemented to estimate base population AF using a generalized least squares (GLS) method when the base population individuals can be assigned to groups. The required dense matrix products involving (A22 )-1v were implemented efficiently using sparse submatrices of A-1, where A and A22 are pedigree relationship matrices for all and genotyped animals, respectively. Three approaches were implemented: iteration on pedigree (IOP), iteration in memory (IM), and direct inversion by sparsity preserving Cholesky decomposition (CHM). The test data had 1.5 million animals genotyped using 50240 markers. Total computing time (the product (A22)-11) was 53 min (1.2 min) by IOP, 51 min (0.3 min) by IM, and 56 min (4.6 min) by CHM. Peak computer core memory use was 0.67 GB by IOP, 0.80 GB by IM, and 7.53 GB by CHM. Thus, the IOP and IM approaches can be recommended for large data sets because of their low memory use and computing time.

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