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This clustering and visualizing of SP 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of SP 500 from variations of close and low prices in historical quotes.

The quantity that we use is the daily variation, 1291 days in 5 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

This clustering and visualizing of SP 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of SP 500 from variations of close and open prices in historical quotes.

The quantity that we use is the daily variation, 1290 days in 5 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

This clustering and visualizing of SP 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of SP 500 from variations of high and low prices in historical quotes.

The quantity that we use is the daily variation, 1289 days in 5 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

This clustering and visualizing of SP 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of SP 500 from variations of close and open prices in historical quotes.

The quantity that we use is the daily variation, 1288 days in 5 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

This clustering and visualizing of SP 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of SP 500 from variations of close and low prices in historical quotes.

The quantity that we use is the daily variation, 1287 days in 5 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

This clustering and visualizing of SP 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of SP 500 from variations of close and open prices in historical quotes.

The quantity that we use is the daily variation, 1286 days in 5 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

This clustering and visualizing of SP 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of SP 500 from variations of high and close prices in historical quotes.

The quantity that we use is the daily variation, 3657 days in 14 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

This clustering and visualizing of SP 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of SP 500 from variations of high and open prices in historical quotes.

The quantity that we use is the daily variation, 3657 days in 14 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

This clustering and visualizing of SP 500 stocks use several unsupervised learning techniques, (Affinity Propagation clustering, Manifold learning techniques ..), to extract the stocks of SP 500 from variations of high and low prices in historical quotes.

The quantity that we use is the daily variation, 3656 days in 14 years, in quote price: quotes that are linked tend to fluctuate in relation to each other during a day.

Stock price values can be predicted from past price data? Yes, Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in stock dataset. This method is often used for dimensionality reduction and analysis of the data.

PCA does reduce from 7 dimensional space of the SP 500 stocks ( 'Date', 'Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume' ), into 2 dimensional space ( 'principal component 1', 'principal component 2' ).