Clustering: A Scikit Learn Tutorial
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This document provides an overview of k-means clustering, a method of unsupervised learning used to group data by measuring distances between data points. The tutorial covers how to implement k-means in the Python library scikit-learn, including parameters for setting clusters and optimizing results. It discusses the advantages and disadvantages of k-means, conditions for its use, and techniques for determining the optimal number of clusters.
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Lesson-3_Program-Outcomes-and-Student-Learning-Outcomes_For-Students.pdf
Lesson-3_Program-Outcomes-and-Student-Learning-Outcomes_For-Students.pdfSarahMaeDuallo tendency and variability. Data visualization techniques.
All the DataOps, all the paradigms .
All the DataOps, all the paradigms .Lars Albertsson Data warehouses, lakes, lakehouses, streams, fabrics, hubs, vaults, and meshes. We sometimes choose deliberately, sometimes influenced by trends, yet often get an organic blend. But the choices have orders of magnitude in impact on operations cost and iteration speed. Let's dissect the paradigms and their operational aspects once and for all.
Clustering: A Scikit Learn Tutorial
- 2. About Me • Chief Data Scientist, WPC Healthcare • Speaker • Researcher • Writer
- 3. Outline • What is k-means clustering? • How does it work? • When is it appropriate to use it? • K-means clustering in scikit-learn • Basic • Basic with adjustments
- 4. Clustering • It is unsupervised learning (inferring a function to describe not so obvious structures from unlabeled data) • Groups data objects • Measures distance between data points • Helps in examining the data
- 5. K-means Clustering • Formally: a method of vector quantization • Informally: a mapping of a large set of inputs to a (countable smaller set) • Separate data into groups with equal variance • Makes use of the Euclidean distance metric
- 6. K-means Clustering Repeats refinement Three basic steps: • Step 1: Choose k (how many groups) • Repeat over: • Step 2: Assignment (labeling data as part of a group) • Step 3: Update This process continues until its goal is reached
- 7. K-means Clustering • Assignment • Update
- 8. K-means Clustering • Advantages • Large data accepted • Fast • Will always find a solution • Disadvantages • Choosing the wrong number of groups • You reach a local optima not a global
- 9. K-means Clustering • When to use • Normally distributed data • Large number of samples • Not too many clusters • Distance can be measured in a linear fashion
- 10. Scikit-Learn • Python • Open-source machine learning library • Very well documented
- 11. Scikit-Learn • Model = EstimatorObject() • Unsupervised: • Model.fit(dataset.data) • dataset.data = dataset
- 12. K-means in Scikit-Learn • Very fast • Data Scientist: picks number of clusters, • Scikit kmeans: finds the initial centroids of groups
- 13. Dataset Name: Household Power Consumption by Individuals Number of attributes: 9 Number of instances: 2,075,259 Missing values: Yes
- 15. K-means in Scikit-Learn • Results
- 16. K-means Parameters • n_clusters • Number of clusters to form • max_iter • Maximum number of repeats for algo in a single run • n_init • Number of times k-means algo will run with different initialization points • init • Method you want to initialize with • precompute_distances • Selection of Yes, No, or let the machine decide • Tol • How tolerable should the algo be when it converges • n_jobs • How many CPUs do you want to engage when running the algo • random_state • What instance should be the starting point for the algo
- 17. n_clusters: choosing k • View the variance • cdist is the distance between sets of observations • pdist is the pairwise distances between observations in the same set
- 18. n_clusters: choosing k Step 1: Determine your k range Step 2: Fit the k-means model for each n_clusters = k Step 3: Pull out the cluster centers for each model
- 19. n_clusters: choosing k Step 4: Calculate Euclidean distance from each point to each cluster center Step 5: Total within-cluster sum of squares Step 6: Total sum of squares Step 7: Difference between-cluster sum of squares
- 20. n_clusters: choosing k • Graphing the variance
- 21. n_clusters: choosing k n_clusters = 4 n_clusters = 7
- 22. n_clusters: choosing k • n_clusters = 8 (default)
- 23. init Methods and their meaning: • k-means++ • Selects initial clusters in a way that speeds up convergence • random • Choose k rows at random for initial centroids • Ndarray that gives initial centers • (n_clusters, n_features)
- 24. K-means (8) n_clusters = 8, init = kmeans++ n_clusters = 8, init = random
- 25. K-means (7) n_clusters = 7, init = kmeans++ n_clusters = 7, init = random
- 26. Comparing Results: Silhouette Score • Silhouette coefficient • Not black and white, lots of gray • Average distance between data observations and other data in cluster • Average distance between data observations and all other points in the NEXT nearest cluster • Silhouette score in scikit-learn • Average silhouette coefficient for all data observations • The closer to 1, the better the fit • Computation time increases with larger datasets
- 27. Result Comparison: Silhouette Score
- 28. What Do the Results Say? • Data patterns may in fact exist • Similar observations can be grouped • We need additional discovery
- 29. A Few Hacks • Clustering is a great way to explore your data and develop intution • Too many features create a problem for understanding • Use dimensionality reduction • Use clustering with other methods
- 30. Let’s Connect • Twitter: @DamianMingle • LinkedIn: DamianRMingle • Sign-up for Data Science Hacks