A Comparative Study on Distance Measuring Approaches for Clustering

Pandit, Shraddha; Gupta, Suchita

doi:10.7815/ijorcs.21.2011.011

Cited by 107 publications

(58 citation statements)

References 2 publications

(2 reference statements)

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“…In this program, we have implemented 4 genomic grouping comparison metrics (or dissimilarity measures), each of which are appropriate for different use cases. If the genomic groupings that comprise a given context set are large, we suggest using either “Common Genes – Dice” or “Common Genes – Jaccard” metrics, which implement the set-based Dice and Jaccard dissimilarity approaches [37], with the individual annotated features within each grouping acting as elements and the whole genomic grouping acting as the set. If genomic groupings contain the same annotated features, however vary in the intergenic spacing between features, we recommend using the “Moving Distances” approach, which uses gene order and intergenic spacing to describe differences between contexts.…”

Section: Resultsmentioning

confidence: 99%

JContextExplorer: a tree-based approach to facilitate cross-species genomic context comparison

2013

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BackgroundCross-species comparisons of gene neighborhoods (also called genomic contexts) in microbes may provide insight into determining functionally related or co-regulated sets of genes, suggest annotations of previously un-annotated genes, and help to identify horizontal gene transfer events across microbial species. Existing tools to investigate genomic contexts, however, lack features for dynamically comparing and exploring genomic regions from multiple species. As DNA sequencing technologies improve and the number of whole sequenced microbial genomes increases, a user-friendly genome context comparison platform designed for use by a broad range of users promises to satisfy a growing need in the biological community.ResultsHere we present JContextExplorer: a tool that organizes genomic contexts into branching diagrams. We implement several alternative context-comparison and tree rendering algorithms, and allow for easy transitioning between different clustering algorithms. To facilitate genomic context analysis, our tool implements GUI features, such as text search filtering, point-and-click interrogation of individual contexts, and genomic visualization via a multi-genome browser. We demonstrate a use case of our tool by attempting to resolve annotation ambiguities between two highly homologous yet functionally distinct genes in a set of 22 alpha and gamma proteobacteria.ConclusionsJContextExplorer should enable a broad range of users to analyze and explore genomic contexts. The program has been tested on Windows, Mac, and Linux operating systems, and is implemented both as an executable JAR file and java WebStart. Program executables, source code, and documentation is available at http://www.bme.ucdavis.edu/facciotti/resources_data/software/.

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Section: Resultsmentioning

confidence: 99%

JContextExplorer: a tree-based approach to facilitate cross-species genomic context comparison

2013

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“…Although the literature on localization methods for WSNs is abundant, proposing novel simple methods that can improve the localization accuracy with low cost is still a research challenge and an important issue for several applications in many domains such as commercial, environmental, health, face recognition, image processing and military domains [8,9,10,11,12,13,14,15].…”

Section: Related Workmentioning

confidence: 99%

A modified Manhattan distance with application for localization algorithms in ad-hoc WSNs

El-Gamel

Dandoush

2015

Ad Hoc Networks

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“…The p -distances corresponding to p = 1, p = 2 and p = ∞ are often called City Block, Euclidean and Chebyshev distance, respectively [24]. Thus, we may call ∆ 1 (r, s), ∆ 2 (r, s), and ∆ ∞ (r, s), City Block, Euclidean and Chebyshev similarity functions, respectively.…”

Section: Generalized Similarity Functionsmentioning

confidence: 99%

An extended vector space model for information retrieval with generalized similarity measures : theory and applications.

Paskaleva¹,

Bochev²,

Ames³

2012

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We present an Extended Vector Space Model (EVSM) for information retrieval, endowed with a new set of similarity functions. Our model considers records as multisets of tokens. A token weight function maps records into a real vectors. Using this vector representation we define a p-norm of a record and pairwise conjunction and disjunction operations on records. These operations prompt consistent extensions of published set-based similarity functions and yield new p distance-based similarities. We demonstrate that some well-known similarities form a subset of the new functions resulting from particular choices of token weights and p-values. In so doing, 3 we establish the equivalence of the corresponding information retrieval models with a properly augmented vector space model. The performance of the extended similarity measures is compared by solving an entity matching (EM) problem for two types of benchmark datasets. Among other things, our results show that the new similarity functions perform particularly well on tasks involving matching of records by keywords.The EVSM served as foundation for mathematically rigorous definition of EM problem. We developed a supervised EM framework that interprets the EM as the combinatorial optimization problem of finding the maximum weight matching in a weighted bipartite graph connecting records from two databases, also known as Linear Sum Assignment Problem (LSAP). Casting of EM problems into LSAP offers valuable practical and theoretical advantages. There are efficient algorithms that solve LSAP in polynomial time. Availability of such algorithms reduces the task of solving the EM problem to computing weights for the edges of the bipartite graph connecting the records from the databases. This allowed focusing efforts on the development of robust and flexible methodologies for the estimation of the similarity between records and led to the notion of an optimal similarity function (OSF) for MMIR problems. The OSF is sought as a linear combination of similarity functions for the common relation attributes. Solution of a suitably defined quadratic program using training data defines the weights in the linear combination. Computational studies using the Abt-Buy e-commerce set and publication databases comprising of research articles in cloud computing, antennas and information retrieval areas confirm the robustness of our approach.4

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A Comparative Study on Distance Measuring Approaches for Clustering

Cited by 107 publications

References 2 publications

JContextExplorer: a tree-based approach to facilitate cross-species genomic context comparison

JContextExplorer: a tree-based approach to facilitate cross-species genomic context comparison

A modified Manhattan distance with application for localization algorithms in ad-hoc WSNs

An extended vector space model for information retrieval with generalized similarity measures : theory and applications.

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