tinkering with clustering lessons

Clustering/1-Visualize/README.md

@@ -6,11 +6,15 @@
 ## [Pre-lecture quiz](link-to-quiz-app)
 ### Introduction
 
-Clustering is a type of unsupervised learning that presumes that a dataset is unlabelled. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data. Clustering is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.
+Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) that presumes that a dataset is unlabelled. It uses various algorithms to sort through unlabeled data and provide groupings according to patterns it discerns in the data. Clustering is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.
 
 ✅ Take a minute to think about the uses of clustering. In real life, clustering happens whenever you have a pile of laundry and need to sort out your family members' clothes 🧦👕👖🩲. In data science, clustering happens when trying to analyze a user's preferences, or determine the characteristics of any unlabeled dataset. Clustering, in a way, helps make sense of chaos.
 
-In real life, clustering can be used to determine things like market segmentation, determining what age groups buy what items, for example. Another use would be anomaly detection, perhaps to detect fraud from a dataset of credit card transactions. Or you might use clustering to determine tumors in a batch of medical scans. Alternately, you could use it for grouping search results - by shopping links, images, or reviews, for example. Clustering is useful when you have a large dataset that you want to reduce and on which you want to perform more granular analysis, so the technique can be used to learn about data before other models are constructed.
+In a professional setting, clustering can be used to determine things like market segmentation, determining what age groups buy what items, for example. Another use would be anomaly detection, perhaps to detect fraud from a dataset of credit card transactions. Or you might use clustering to determine tumors in a batch of medical scans. 
+
+> Interestingly, Cluster Analysis originated in the fields of Anthropology and Psychology in the 1930s. Can you imagine how it might have been used?
+
+Alternately, you could use it for grouping search results - by shopping links, images, or reviews, for example. Clustering is useful when you have a large dataset that you want to reduce and on which you want to perform more granular analysis, so the technique can be used to learn about data before other models are constructed.
 
 ✅ Once your data is organized in clusters, you assign it a cluster Id, and this technique can be useful when preserving a dataset's privacy; you can instead refer to a data point by its cluster id, rather than by more revealing identifiable data. Can you think of other reasons why you'd refer to a cluster Id rather than other elements of the cluster to identify it?
 ## Getting started with clustering
@@ -51,9 +55,35 @@ In real life, clustering can be used to determine things like market segmentatio
 > 🎓 'Density'
 > 
 > Data that is 'noisy' is considered to be 'dense'. The distances between points in each of its clusters may prove, on examination, to be more or less dense, or 'crowded' and thus this data needs to be analyzed with the appropriate clustering method. [This article](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) demonstrates the difference between using K-Means clustering vs. HDBSCAN algorithms to explore a noisy dataset with uneven cluster density.
+
+### Clustering Algorithms
+
+There are over 100 clustering algorithms, and their use depends on the nature of the data at hand. Let's discuss some of the major ones:
+
+**Hierarchical clustering** 
+
+If an object is classified by its proximity to a nearby object, rather than to one farther away, clusters are formed based on their members' distance to and from other objects. Scikit-Learn's Agglomerative clustering is hierarchical.
+
+TODO:  infographic
+
+**Centroid clustering** 
+
+This popular algorithm requires the choice of 'k', or the number of clusters to form, after which the algorithm determines the center point of a cluster and gathers data around that point. [K-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) is a popular version of centroid clustering. The center is determined by the nearest mean, thus the name. The squared distance from the cluster is minimized.
+
+**Distribution-based clustering**
+
+Based in statistical modeling, distribution-based clustering centers on determining the probability that a data point belongs to a cluster, and assigning it accordingly. Gaussian Mixture methods belong to this type.
+
+**Density-based clustering**
+
+Data points are assigned to clusters based on their density, or their grouping around each other. Data points far from the group are considered outliers or noise. DBSCAN, Mean-shift and OPTICS belong to this type of clustering.
+
+**Grid-based clustering**
+
+For multi-dimensional datasets, a grid is created and the data is divided amongst the grid's cells, thereby creating clusters.
 ### Preparation
 
-Clustering is heavily dependent on visualization, so let's get started.
+Clustering is heavily dependent on visualization, so let's get started by visualizing our music data. This exercise will help us decide which of the methods of clustering we should most effectively use for the nature of this data.
 
 Open the notebook.ipynb file in this folder and append the song data .csv file. Load up a dataframe with some data about the songs. Get ready to explore this data by importing the libraries and dumping out the data:
 
@@ -183,6 +213,10 @@ Explore the data by checking the most popular genre:
 
 ## Review & Self Study
 
-Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
+Before you apply clustering algorithms, as we have learned, you must determine the nature of your dataset. Read more onn this topic [here](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)
+
+[This helpful article](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/) walks you through the different ways that various clustering algorithms behave given different data shapes
+
+In the next lesson, you will make use of the  most popular clustering method, K-Means. Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
 
 **Assignment**: [Assignment Name](assignment.md)