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Clustering for New Discovery in Data Part 2 Houston Machine Learning Meetup

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Page 1: Spectral clustering - Houston ML Meetup

Clustering for New Discovery in DataPart 2

Houston Machine Learning Meetup

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https://energyconferencenetwork.com/machine-learning-oil-gas-2017/

20% off, PROMO code: HML

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Roadmap: Method

• Tour of machine learning algorithms (1 session)• Feature engineering (1 session)

– Feature selection - Yan

• Supervised learning (4 sessions)– Regression models -Yan– SVM and kernel SVM - Yan– Tree-based models - Dario– Bayesian method - Xiaoyang– Ensemble models - Yan

• Unsupervised learning (3 sessions)– K-means clustering – DBSCAN - Cheng– Mean shift – Agglomerative clustering – Kunal– Spectral clustering – Yan– Dimension reduction for data visualization - Yan

• Deep learning (4 sessions) _ Neural network

– From neural network to deep learning – Convolutional neural network– Train deep nets with open-source tools

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Roadmap: Application

• Business analytics

• Recommendation system

• Natural language processing

• Computer vision

• Energy industry

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Clustering Algorithm

• K-Means (King of clustering, many variants)

• DBSCAN (group neighboring points)

• Hierarchical clustering (a hierarchical structure, multiple levels)

• Mean shift (locating the maxima of density)

• Spectral clustering (cares about connectivity instead of proximity)

• Expectation Maximization (k-means is a variant of EM)

• Latent Dirichlet Allocation (natural language processing)

……

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Agenda

• Hierarchical clustering

• Mean shift

• Spectral clustering

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Referenece: http://www.cvl.isy.liu.se:82/education/graduate/spectral-clustering.html

Spectral Clustering

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Add noise to A

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Modification I

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Min-cut problem

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Modification II

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Ratio-cut problem

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Normalized spectral clustering

Make the clustering less sensitive to the cluster sizes

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Normalized symmetric spectral clustering

Less sensitive to the cluster sizes and better separation of clusters

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Summary

• Application of spectral clustering in computer vision

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Roadmap: Method

• Tour of machine learning algorithms (1 session)• Feature engineering (1 session)

– Feature selection - Yan

• Supervised learning (4 sessions)– Regression models -Yan– SVM and kernel SVM - Yan– Tree-based models - Dario– Bayesian method - Xiaoyang– Ensemble models - Yan

• Unsupervised learning (3 sessions)– K-means clustering – DBSCAN - Cheng– Mean shift – Agglomerative clustering – Spectral clustering – Kunal, Yan– Dimension reduction for data visualization - Yan

• Deep learning (4 sessions) _ Neural network

– From neural network to deep learning – Convolutional neural network– Train deep nets with open-source tools

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https://energyconferencenetwork.com/machine-learning-oil-gas-2017/

20% off, PROMO code: HML

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Thank you

Slides will be posted on slide share:

http://www.slideshare.net/xuyangela

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Parking We are here @ W205

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