Subspace Clustering Without Knowing the Number of Clusters: A Parameter Free Approach

Menon, Vishnu; Muthukrishnan, Gokularam; Kalyani, Sheetal

doi:10.1109/tsp.2020.3018665

Cited by 13 publications

(7 citation statements)

References 54 publications

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“…The M-Rock [14] algorithm improves the computing time of the ROCK [15] algorithm by introducing a new goodness measure and criterion function, the former for computing intra similarity between identities and the latter for effective cluster merging. Depending on the dataset, it selects the best intra similarity measure from Modified Sorensen Dice coefficient, Modified Traversky, and Modified second Kulczynski.…”

Section: M-rockmentioning

confidence: 99%

P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Altameem¹,

Poonia²,

Kumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to expansions, several new clustering methods and extensions have been proposed in recent years. ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them. This paper aims to modify the algorithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures. The parameterized ROCK algorithm is the name given to the modified algorithm (P-ROCK). The proposed modification makes the original algorithm more flexible by using user-defined parameters. A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm. A comparison with the original ROCK algorithm is also provided. Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%. The proposed P-ROCK algorithm has improved the runtime and is more flexible and scalable.

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Section: M-rockmentioning

confidence: 99%

P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Altameem¹,

Poonia²,

Kumar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Besides, it is important that the second level algorithm should be unsupervised because the number of clusters (defects) are unknown. In recent studies, 43,44 a few approaches for unsupervised clustering have been discussed. A simple density-based clustering approach would be sufficient in our case since the spurious responses are not as dense as those in the defective region.…”

Section: Approachmentioning

confidence: 99%

“…In recent studies, 43,44 a few approaches for unsupervised clustering have been discussed. A simple density-based clustering approach would be sufficient in our case since the spurious responses are not as dense as those in the defective region.…”

Section: Approachmentioning

confidence: 99%

Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves

Jagadeeshwar

Kalyani

Rajagopal

et al. 2022

Structural Health Monitoring

Self Cite

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Delamination in composite structures is characterized by a resonant cavity wherein a fraction of an ultrasonic guided wave may be trapped. Based on this wave trapping phenomenon, we propose a baseline-free statistical approach for the identification and localization of delamination using sparse sampling and density-based spatial clustering of applications with noise (DBSCAN) technique. The proposed technique can be deployed for rapid inspection with minimal human intervention. The Performance of the proposed technique in terms of its ability to determine the precise location of such defects is quantified through the probability of detection measurements. The robustness of the proposed technique is tested through extensive simulations consisting of different random locations of defects on flat plate structures with different sizes and orientation as well as different values of signal to noise ratio of the simulated data. The simulation results are also validated using experimental data and the results are found to be in good agreement.

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“…Some other recent works in the literature also include the work done in subspace clustering to deal with high dimensional datasets. Menon et al [60] proposed a parameterfree approach in the subspace clustering, where the data are clustered based on statistical distribution within a subspace. The performance of the algorithm is also shown on various public datasets.…”

Section: Clustering Strategiesmentioning

confidence: 99%

Bootstrap–CURE: A Novel Clustering Approach for Sensor Data—An Application to 3D Printing Industry

2022

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The agenda of Industry 4.0 highlights smart manufacturing by making machines smart enough to make data-driven decisions. Large-scale 3D printers, being one of the important pillars in Industry 4.0, are equipped with smart sensors to continuously monitor print processes and make automated decisions. One of the biggest challenges in decision autonomy is to consume data quickly along the process and extract knowledge from the printer, suitable for improving the printing process. This paper presents the innovative unsupervised learning approach, bootstrap–CURE, to decode the sensor patterns and operation modes of 3D printers by analyzing multivariate sensor data. An automatic technique to detect the suitable number of clusters using the dendrogram is developed. The proposed methodology is scalable and significantly reduces computational cost as compared to classical CURE. A distinct combination of the 3D printer’s sensors is found, and its impact on the printing process is also discussed. A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented.

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Subspace Clustering Without Knowing the Number of Clusters: A Parameter Free Approach

Cited by 13 publications

References 54 publications

P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves

Bootstrap–CURE: A Novel Clustering Approach for Sensor Data—An Application to 3D Printing Industry

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