Unsupervised Learning and Clustering

Greene, Derek; Cunningham, Pádraig; Mayer, Rudolf

doi:10.1007/978-3-540-75171-7_3

Cited by 70 publications

(37 citation statements)

References 54 publications

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“…At the same time, unsupervised ML, also known as "learning without a teacher", is a type of learning where patterns are to be discovered from unknown data [44,45]. In this case, there is only training data, and the aim is to group objects into clusters and/or reduce a large amount of the given data.…”

Section: Diagnostic Possibilities With Machine Learningmentioning

confidence: 99%

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

et al. 2021

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A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes.

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Section: Diagnostic Possibilities With Machine Learningmentioning

confidence: 99%

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

et al. 2021

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“…Unsupervised learning applies techniques on the input data instances to mine useful information, detect patterns and group the data instances so that valuable insights are obtained [63,72]. These techniques include K-Means Clustering, Apriori and FP Growth.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

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The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proﬁcient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identiﬁed key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented. Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.

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“…In machine learning, supervised techniques are used when datasets are labelled, for example, in the case where the identity of each calling individual is known, and calls can be subsequently classified based on known class membership (Greene et al., 2008). Supervised methods of discriminating between primate individuals based on features of their calls have been well established (Clink et al., 2017; Leliveld et al., 2011; Mielke & Zuberbühler, 2013; Mitani et al., 1996; Rendall, 2003; Rendall et al., 1996; Terleph et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

“…An important caveat for the use of supervised methods is that individual identity must be known, which is often not the case with acoustic data collected autonomously over extended periods. Generally, unsupervised clustering algorithms are used to make inferences about unlabelled data, which is in contrast to supervised algorithms that require the input of labelled training data (Dinov, 2018; Greene et al., 2008).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring

Clink

Klinck

2020

Methods Ecol Evol

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Passive acoustic monitoring (PAM) has the potential to greatly improve our ability to monitor cryptic yet vocal animals. Advances in automated signal detection have increased the scope of PAM, but distinguishing between individuals—which is necessary for density estimation—remains a major challenge. When individual identity is known, supervised classification techniques can be used to distinguish between individuals. Supervised methods require labelled training data, whereas unsupervised techniques do not. If the acoustic signals of individuals are sufficiently different, the number of clusters might represent the number of individuals sampled. The majority of applications of unsupervised techniques in animal vocalizations have focused on quantifying species‐specific call repertoires. However, with increased interest in PAM applications, unsupervised methods that can distinguish between individuals are needed. Here we use an existing dataset of Bornean gibbon female calls with known identity from five sites on Malaysian Borneo to test the ability of three different unsupervised clustering algorithms (affinity propagation, K‐medoids and Gaussian mixture model‐based clustering) to distinguish between individuals. Calls from different gibbon females are readily distinguishable using supervised techniques. For internal validation of unsupervised cluster solutions, we calculated silhouette coefficients. For external validation, we compared clustering results with female identity labels using a standard metric: normalized mutual information. We also calculated classification accuracy by assigning unsupervised cluster solutions to females based on which cluster had the highest number of calls from a particular female. We found that affinity propagation clustering consistently outperformed the other algorithms for all metrics used. In particular, classification accuracy of affinity propagation clustering was more consistent as the number of females increased, and when we randomly sampled females across sites. We conclude that unsupervised techniques may be useful for providing additional information regarding individual identity for PAM applications. We stress that although we use gibbons as a case study, these methods will be applicable for any individually distinct vocal animal.

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Unsupervised Learning and Clustering

Cited by 70 publications

References 54 publications

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

Data Analytics in the Internet of Things: A Survey

Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring

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