A Novel Heuristic for Evolutionary Clustering

Nerurkar, Pranav; Shirke, Archana; Chandane, Madhav; Bhirud, S. G.

doi:10.1016/j.procs.2017.12.100

Cited by 19 publications

(7 citation statements)

References 11 publications

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“…The number of clusters in c-means has been chosen as an internal index by the Bayesian information criteria (Shao et al, 2016;Nerurkar et al, 2018). This suggests that the number of clusters chosen by c-means is nearly optimal.…”

Section: Organization and Implementationmentioning

confidence: 99%

“…One of the existing issues is selecting acceptable methods for clustering this type of data, as well as evaluating the quality of spatiotemporal data clustering. There are numerous methods for evaluating clustering quality, including Silhouette, Davis Boldin, and index C (Shao et al, 2016;Thrun,2018;Nerurkar et al, 2018;Rousseeuw, 1987;Sun et al, 2010), but these approaches are not appropriate for spatiotemporal data since they use only one criterion to identify the best partition. While analyzing and evaluating spatiotemporal data require considering the distance between the two spatial and temporal domains (Shao et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy Clustering Based Method for the Spatio-Temporal Data Analysis

Zolghadr¹,

Jalalian²

2022

Journal of Computer Science

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Spatiotemporal data is a type of data that is collected by the sensors. This type of data has two spatial and temporal dimensions. There are many challenges in analyzing spatiotemporal big data. Common evaluation metrics of clustering methods are not appropriate for spatiotemporal data. Previous clustering methods and the conventional evaluation metrics are efficient for data like time series with only one segment. Therefore, other metrics are required to evaluate the clustering of such data. In this study, energy function, reconstruction, and prediction metrics are used to evaluate the quality of spatiotemporal data clustering. The purpose of this study is to minimize the energy function using the Fuzzy C-Mean method on spatiotemporal data. The obtained results are compared with those obtained using k-medoid, DBSCAN, COBWEB, X-means, and TLBO. Also, the energy function, reconstruction, and prediction metrics are used to evaluate the quality of the clusters. The clustering methods are implemented on the dataset of parking located in the CBD area of Australia.

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Section: Organization and Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fuzzy Clustering Based Method for the Spatio-Temporal Data Analysis

Zolghadr¹,

Jalalian²

2022

Journal of Computer Science

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“…Nerurkar et al [40] proposed a new cost function for the original PSO model for clustering analysis. Their fitness function took both intra-and inter-cluster distances into account, with a linear computational cost.…”

Section: Hybrid Clustering and Classification Modelsmentioning

confidence: 99%

Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks

Tan

Zhang

Lim

2020

Knowledge-Based Systems

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In this research, we propose a variant of the Particle Swarm Optimization (PSO) algorithm, namely hybrid learning PSO (HLPSO), for skin lesion segmentation and classification. HLPSO combines diverse search mechanisms including modified Firefly Algorithm (FA) operations, a new spiral research action, probability distributions, crossover, and mutation procedures to diversify and improve the original PSO algorithm. It is used in conjunction with the K-Means clustering algorithm to enhance lesion segmentation. Its cost function takes both intra-class and inter-class variations into account to increase scalability. Two lesion classification systems are formulated based on HLPSO. In the first system, HLPSO is used to devise evolving convolutional neural networks (CNN) with optimized topologies and hyper-parameters for lesion classification. In the second system, shape and colour features, as well as texture features extracted using the Kirsch operator and Shift Local Binary Patterns are used to produce an initial discriminative lesion representation. HLPSO is then used to identify the most significant components of each feature vector for ensemble lesion classification. Evaluated using several skin lesion data sets, both systems depict superior capabilities in lesion segmentation, deep CNN architecture generation, and discriminative feature selection for ensemble lesion classification, and outperform a number of advanced PSO and FA variants, classical search methods, as well as other related models on skin lesion classification significantly. HLPSO also yields better performances over other classical and advanced search methods in solving a number of benchmark tasks related to mathematical landscapes and those in the complex CEC 2014 test suite.

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“…algorithms, which learn complex and nonlinear representative and discriminative features in a tiered manner from the inputs, are an emerging field in the artificial intelligence community and have entered into the satellite image processing community for R.S. unstructured data analysis 15‐17 . This new field of investigation opened by deep learning has led to mushrooming of literature and frameworks 4,12 .…”

Section: Introductionmentioning

confidence: 99%

Multilabel classification of remote sensed satellite imagery

Kumar

Abhishek

Singh

et al. 2020

Trans Emerging Tel Tech

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Multilabel scene classification has emerged as a critical research area in the domain of remote sensing. Contemporary classification models primarily emphasis on a single object or multiobject scene classification of satellite remote sensed images. These classification models rely on feature engineering from images, deep learning, or transfer learning. Comparatively, multilabel scene classification of very high resolution (V.H.R.) images is a fairly unexplored domain of research. Models trained for single label scene classification are unsuitable for the application of recognizing multiple objects in a single remotely sensed V.H.R. satellite image. To overcome this research gap, the current inquiry proposes to fine‐tune the state of the art convolutional neural network (C.N.N.) architectures for multilabel scene classification. The proposed approach pre trains C.N.N on the ImageNet dataset and further fine‐tunes them to the task of detecting multiple objects in V.H.R. images. To understand the efficacy of this approach, the final models are applied on a V.H.R. dataset: the U.C.M.E.R.C.E.D. image dataset containing 21 different terrestrial land use categories with a submeter resolution. The performance of the final models is compared with graph convolutional network‐based model by Khan et al. From the results on performance metrics, it was observed that proposed models achieve comparable results in significantly fewer epochs.

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A Novel Heuristic for Evolutionary Clustering

Cited by 19 publications

References 11 publications

A Fuzzy Clustering Based Method for the Spatio-Temporal Data Analysis

A Fuzzy Clustering Based Method for the Spatio-Temporal Data Analysis

Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks

Multilabel classification of remote sensed satellite imagery

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