Local and Global Data Spread Based Index for Determining Number of Clusters in a Dataset

Riyaz, Romana; Wani, M. Arif

doi:10.1109/icmla.2016.0115

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…Not all internal indexes measure compactness and separation (Riyaz & Wani, 2016). In our experimentation, we choose VIC (Rodríguez et al, 2018), an internal index whose design is not focused on the shape of the clusters but on the rationale that a good partition also yields a good classification model.…”

Section: Internal External and Relative Indexesmentioning

confidence: 99%

Cluster validation in clustering‐based one‐class classification

Rodríguez¹,

Monroy²,

Medina‐Pérez³

et al. 2019

Expert Systems

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Reconstruction‐based one‐class classification has shown to be very effective in a number of domains. This approach works by attempting to capture the underlying structure of the normal class, typically, by means of clusters of objects. It has the main disadvantage, however, that one has to indicate the number of clusters in advance, for this yields an efficient way of computing a clustering. In this paper, we introduce a new algorithm, OCKRA++, which achieves a better performance, by enhancing a clustering‐based one‐class ensemble classifier (OCKRA) with a cluster validity index that is used to set the best number of clusters during the classifier's training process. We have thoroughly tested OCKRA++ in a particular domain, namely masquerade detection. For this purpose, we have used the Windows‐Users and ‐Intruder simulation Logs data set repository, which contains 70 different masquerade data sets. We have found that OCKRA++ is currently the algorithm that achieves the best area under the curve, with a significant difference, in masquerade detection using the file system navigation approach.

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Section: Internal External and Relative Indexesmentioning

confidence: 99%

Cluster validation in clustering‐based one‐class classification

Rodríguez¹,

Monroy²,

Medina‐Pérez³

et al. 2019

Expert Systems

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“…This provides a procedure of identifying the regions that encode proteins. The protein homology detection also takes an account of recognizing the patterns in multidimensional data [22]. Such a region is said to be an open reading frame (ORF) [23] which assembles like a gene but it has not been proved to be a gene yet.…”

mentioning

confidence: 99%

PSSM amino-acid composition based rules for gene identification

Bhat¹,

Wani²

2018

IJATEE

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With the advancement of genome sequence data, a large number of gene predicting programs have come into existence. Big data associated with the problem of identification of genes can be projected into subspaces or clusters so that the given problem can be divided into sub-problems. Each sub-problem can then be optimized with an appropriate model. One of the approaches of dividing a given problem into subproblems involves projecting the big data to various sub-space grids [1−4], where each sub-space grid represents a sub-problem. Each sub-problem can then be represented independently with various models which can be combined by using a rule based system [5].The gene identification from large genome sequence is found to be one of the significant issues to solve in the field of bioinformatics. There is an essential requirement of developing gene finding methods and their corresponding functions. One of the foremost problems in the process of gene forecasting is to discover the protein coding genes in genomic DNA sequence.

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Automatic Solar Panel Detection from High-Resolution Orthoimagery Using Deep Learning Segmentation Networks

Mujtaba

Wani

2020

Advances in Intelligent Systems and Computing

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Local and Global Data Spread Based Index for Determining Number of Clusters in a Dataset

Cited by 11 publications

References 18 publications

Cluster validation in clustering‐based one‐class classification

Cluster validation in clustering‐based one‐class classification

PSSM amino-acid composition based rules for gene identification

Automatic Solar Panel Detection from High-Resolution Orthoimagery Using Deep Learning Segmentation Networks

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