The MST-kNN with Paracliques

Arefin, Ahmed Shamsul; Riveros, Carlos; Berretta, Regina; Moscato, Pablo

doi:10.1007/978-3-319-14803-8_29

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…That study resulted in a set of marker words that can distinguish between plays written by Fletcher, Jonson, Middleton and Shakespeare compared to the remainder of the authors. Recently, Arefin et al [ 31 ] used this dataset to test the performance of their new graph clustering method incorporating paraclique identification and a clustering method known as MST- k NN [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays

et al. 2016

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In this study we propose a novel, unsupervised clustering methodology for analyzing large datasets. This new, efficient methodology converts the general clustering problem into the community detection problem in graph by using the Jensen-Shannon distance, a dissimilarity measure originating in Information Theory. Moreover, we use graph theoretic concepts for the generation and analysis of proximity graphs. Our methodology is based on a newly proposed memetic algorithm (iMA-Net) for discovering clusters of data elements by maximizing the modularity function in proximity graphs of literary works. To test the effectiveness of this general methodology, we apply it to a text corpus dataset, which contains frequencies of approximately 55,114 unique words across all 168 written in the Shakespearean era (16th and 17th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays.

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Section: Methodsmentioning

confidence: 99%

A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays

et al. 2016

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“…The time complexity of such procedures-taking into account that the neighborhood is to be identified for all query points-is O(N 2 ) + (N 2 log N ). It becomes prohibitive for most practical applications (Arefin et al, 2012).…”

Section: : Denote Bymentioning

confidence: 99%

Efficient Astronomical Data Condensation Using Approximate Nearest Neighbors

Łukasik

Lalik

Sarna

et al. 2019

International Journal of Applied Mathematics and Computer Science

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Extracting useful information from astronomical observations represents one of the most challenging tasks of data exploration. This is largely due to the volume of the data acquired using advanced observational tools. While other challenges typical for the class of big data problems (like data variety) are also present, the size of datasets represents the most significant obstacle in visualization and subsequent analysis. This paper studies an efficient data condensation algorithm aimed at providing its compact representation. It is based on fast nearest neighbor calculation using tree structures and parallel processing. In addition to that, the possibility of using approximate identification of neighbors, to even further improve the algorithm time performance, is also evaluated. The properties of the proposed approach, both in terms of performance and condensation quality, are experimentally assessed on astronomical datasets related to the GAIA mission. It is concluded that the introduced technique might serve as a scalable method of alleviating the problem of the dataset size.

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“…Several clustering algorithms for gene expression data have been proposed during the last years [ 16 – 24 ]. They are based on guilt-by-association paradigm [ 25 ], i.e.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies

2018

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BackgroundBiologists aim to understand the genetic background of diseases, metabolic disorders or any other genetic condition. Microarrays are one of the main high-throughput technologies for collecting information about the behaviour of genetic information on different conditions. In order to analyse this data, clustering arises as one of the main techniques used, and it aims at finding groups of genes that have some criterion in common, like similar expression profile. However, the problem of finding groups is normally multi dimensional, making necessary to approach the clustering as a multi-objective problem where various cluster validity indexes are simultaneously optimised. They are usually based on criteria like compactness and separation, which may not be sufficient since they can not guarantee the generation of clusters that have both similar expression patterns and biological coherence.MethodWe propose a Multi-Objective Clustering algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) to find clusters of genes with high levels of co-expression, biological coherence, and also good compactness and separation. Cluster quality indexes are used to optimise simultaneously gene relationships at expression level and biological functionality. Our proposal also includes intensification and diversification strategies to improve the search process.ResultsThe effectiveness of the proposed algorithm is demonstrated on four publicly available datasets. Comparative studies of the use of different objective functions and other widely used microarray clustering techniques are reported. Statistical, visual and biological significance tests are carried out to show the superiority of the proposed algorithm.ConclusionsIntegrating a-priori biological knowledge into a multi-objective approach and using intensification and diversification strategies allow the proposed algorithm to find solutions with higher quality than other microarray clustering techniques available in the literature in terms of co-expression, biological coherence, compactness and separation.

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The MST-kNN with Paracliques

Cited by 4 publications

References 22 publications

A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays

A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays

Efficient Astronomical Data Condensation Using Approximate Nearest Neighbors

A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies

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