Finding Groups in Data: An Introduction to Cluster Analysis

Hadi, Ali S.; Kaufman, L.; Rousseeuw, Peter J.

doi:10.2307/1269576

Cited by 525 publications

(695 citation statements)

References 0 publications

Supporting

Mentioning

653

Contrasting

Unclassified

Order By: Relevance

“…This clustering method relies on the medoid concept [34]. A medoid, in this case a cluster medoid, is the point of the cluster whose value is the closest to the mean value of the whole cluster, or, in other words, the most central point of the cluster.…”

Section: K-medoidsmentioning

confidence: 99%

A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

et al. 2021

View full text Add to dashboard Cite

Continued advances in machine learning (ML) and deep learning (DL) present new opportunities for use in a wide range of applications. One prominent application of these technologies is defect detection and classification in the manufacturing industry in order to minimise costs and ensure customer satisfaction. Specifically, this scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL techniques and configurations have been used for defect detection and classification. Inspection operations have traditionally been carried out by specialised personnel in charge of visually judging the images obtained with a scanning electron microscope (SEM). This scoping review focuses on inspection operations in the semiconductor manufacturing industry where different ML and DL methods have been used to detect and classify defects in SEM images. We also include the performance results of the different techniques and configurations described in the articles found. A thorough comparison of these results will help us to find the best solutions for future research related to the subject.

show abstract

Section: K-medoidsmentioning

confidence: 99%

A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Once the classification tool was developed, it was also necessary to determine whether an unsupervised approach would yield a breakdown of topics similar to that of the AskNature taxonomy [43] keeping in mind that there may be overlap in terms or an alternative way to view topics. Exploratory analysis of the text composition was carried out through an unsupervised application of a random forest machine learning algorithm, which was used to generate a proximity matrix to apply towards a partition around medoids (PAM) algorithm [45]. The optimal k or number of clusters was analyzed, and a k = 8 was selected to determine the degree of possible concordance between the manually assigned labels and the PAM clusters.…”

Section: Taxonomymentioning

confidence: 99%

PeTaL (Periodic Table of Life) and Physiomimetics

Friend

Whiteaker

Bense

et al. 2019

Designs

View full text Add to dashboard Cite

The Periodic Table of Life (PeTaL) is a system design tool and open source framework that uses artificial intelligence (AI) to aid in the systematic inquiry of nature for its application to human systems. This paper defines PeTaL’s architecture and workflow. Biomimicry, biophysics, biomimetics, bionics and numerous other terms refer to the use of biology and biological principles to inform practices in other disciplines. For the most part, the domain of inquiry in these fields has been confined to extant biological models with the proponents of biomimicry often citing the evolutionary success of extant organisms relative to extinct ones. An objective of this paper is to expand the domain of inquiry for human processes that seek to model those that are, were or could be found in nature with examples that relate to the field of aerospace and to spur development of tools that can work together to accelerate the use of artificial intelligence, topology optimization and conventional modeling in problem solving. Specifically, specialized fields such as paleomimesis, anthropomimesis and physioteleology are proposed in conjunction with artificial evolution. The overarching philosophy outlined here can be thought of as physiomimetics, a holistic and systematic way of learning from natural history. The backbone of PeTaL integrates an unstructured database with an ontological model consisting of function, morphology, environment, state of matter and ecosystem. Tools that support PeTaL include machine learning, natural language processing and computer vision. Applications of PeTaL include guiding human space exploration, understanding human and geological history, and discovering new or extinct life. Also discussed is the formation of V.I.N.E. (Virtual Interchange for Nature-inspired Exploration), a virtual collaborative aimed at generating data, research and applications centered on nature. Details of implementation will be presented in subsequent publications. Recommendations for future work are also presented.

show abstract

“…While SED is not metric due to being asymmetric, it satisfies triangle inequality, non-negativity, and subgraphidentity. Several higher-order tasks such as clustering and indexing rely on such properties [21,15,24,43,46,50,52,10,12]. Existing neural approaches do not preserve these properties, which limits their usability for these higher order tasks.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Neural Framework for Learning Subgraph and Graph Similarity Measures

Sharma¹,

Grover²,

Medya³

et al. 2021

Preprint

View full text Add to dashboard Cite

Subgraph similarity search is a fundamental operator in graph analysis. In this framework, given a query graph and a graph database, the goal is to identify subgraphs of the database graphs that are structurally similar to the query. Subgraph edit distance (SED) is one of the most expressive measures for subgraph similarity. In this work, we study the problem of learning SED from a training set of graph pairs and their SED values. Towards that end, we design a novel siamese graph neural network called NEUROSED, which learns an embedding space with a rich structure reminiscent of SED. With the help of a specially crafted inductive bias, NEUROSED not only enables high accuracy but also ensures that the predicted SED, like true SED, satisfies triangle inequality. The design is generic enough to also model graph edit distance (GED), while ensuring that the predicted GED space is metric, like the true GED space. Extensive experiments on real graph datasets, for both SED and GED, establish that NEUROSED achieves ≈ 2 times lower RMSE than the state of the art and is ≈ 18 times faster than the fastest baseline. Further, owing to its pair-independent embeddings and theoretical properties, NEUROSED allows ≈ 3 orders of magnitude faster retrieval of graphs and subgraphs.Preprint. Under review.

show abstract

Finding Groups in Data: An Introduction to Cluster Analysis

Cited by 525 publications

References 0 publications

A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

PeTaL (Periodic Table of Life) and Physiomimetics

A Neural Framework for Learning Subgraph and Graph Similarity Measures

Contact Info

Product

Resources

About