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I sorted this using a Jupyter package called ipyleaflet, which is a python plugin to the javascript mapping library leaflet. You can add/remove layers from the map while the widget is running, which is exactly what I needed.


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Step 1: calculate the euclidean distance for each data point. d(d1,d2) : 3.162 d(d1,d3) : 5.094 d(d1,d4) : 2 d(d1,d5) : 4.24 d(d2,d1) : 3.162 d(d2,d3) : 2.828 d(d2,d5) : 4.24 d(d3,d2) : sqrt(8) d(d3,d4) : 3.162 d(d2,d5) : 1.41 d(d4,d5) : 4.47 Step 2: group the data points which are closer to the centroid. d1 is closer to d4 d3 is closer to d2 d5 is closer to ...


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Clustering evaluation metrics basic goal is to measure the similarity within each cluster and dissimilarity among clusters; if a clustering algorithm has achieved those two things in an acceptable degree than it has performed well. The most commonly used evaluation metrics are: -Silhouette Coefficient - it is the most popular one for time-series clustering (...


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The intuition is to choose a point that is as far as possible from the existing centers. It does not matter in which direction the new point lies, as long as it is far away.


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I think the k-prototype algorithm is what you are looking for. https://link.springer.com/article/10.1023/A:1009769707641


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This specific format to me looks like graphviz. So if you can extract the tree edges from your original object, then you can render it, example below (some roundabout to convert between different objects): import networkx as nx import pydot import graphviz # Just a part of your graph G = nx.Graph() ed = [('n3','n0'), ('n0','MusicHendrixA'), ('n0'...


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One option is to change hashing functions to a function that is more likely to have collisions. For example, Pearson hashing is an 8-bit hash that will have far more collisions than more common hashing functions.


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The problem is often framed in the inverse - find bivariate features with high correlation which are then removed from a model to increase interpretability and allow certain models to be fit. This is commonly called multicollinearity.


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You may be able to find some non-linear embedding of your data which better clusters your data. This would assume that your data actually sits in some lower-dimensional space which linear methods like PCA are not powerful enough to recover. Sklearn.manifold would be a good place to start, e.g isomap or tSNE.


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If the data is unlabeled, you'll have to apply clustering. One useful way to frame the problem is as time-series clustering. Almost all clustering algorithms have a time-series version (e.g., k-means and hierarchical). The choice of clustering algorithm depends both on the type of data available and purpose of the project.


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