Unsupervised learning is a class of machine learning techniques used to discover patterns in data. This article describes several clustering algorithms for unsupervised learning in Python, including K-Means clustering, hierarchical clustering, t-SNE clustering, and DBSCAN clustering.
Unsupervised algorithm data is not labeled, which means that only the input variable (X) is provided and there is no corresponding output variable. In unsupervised learning, the algorithm itself finds a meaningful structure in the data.
Yan Lecun, chief AI scientist at Facebook, explained that unsupervised learning—teaching machines to learn on their own—does not need to explicitly tell them whether everything they do is right or wrong, and is the key to “real†AI.
Supervised learning vs unsupervised learning
In supervised learning, the system attempts to learn from the examples given earlier. Conversely, in unsupervised learning, the system attempts to find patterns directly from the given examples. Therefore, if the dataset has a tag, then it is a supervised problem. If the dataset has no tag, then it is an unsupervised problem.
As shown above, the left is an example of supervised learning; we use regression techniques to find the best-fit line between features. In unsupervised learning, input is based on feature separation, and prediction depends on which cluster it belongs to.
Important terms
Feature: The input variable used to make the prediction.
Predictions: The output of the model when an input sample is provided.
Example: One row of a data set. An example contains one or more features that may have labels.
Label: The result of the feature.
Prepare for unsupervised learning
In this article, we use the Iris data set (Iris flower data set) for our first prediction. The dataset contains a set of data for 150 records and has 5 attributes - petal length, petal width, sepal length, sepal width, and category. The three categories are Iris Setosa, Iris Virginica, and Iris Versicolor. For our unsupervised algorithm, we give these four characteristics of Iris and predict which one it belongs to. We use the sklearn library in Python to load the Iris dataset and use matplotlib for data visualization. The following is a code snippet.
Violet: Mountain Iris, Green: Virginia Iris, Yellow: Iris
Clustering
In clustering, the data is divided into several groups. Simply stated, the purpose is to open components with similar characteristics and group them into clusters.
Visualization example:
In the figure above, the image on the left is the original data that has not been classified, and the image on the right is clustered (the data is classified according to the characteristics of the data). When given the input to be predicted, it will be checked in its own cluster based on its characteristics and make predictions.
K-Means Clustering in Python
K-Means is an iterative clustering algorithm whose purpose is to find the local maximum in each iteration. First, select the desired number of clusters. Since we already know that 3 classes are involved, we have grouped the data into 3 classes by passing the parameter "n_clusters" into the K-Means model.
Now, three points (input) are randomly divided into three clusters. Based on the centroid distance between each point, the next given input is divided into the desired clusters. Then, recalculate the centroids of all clusters.
Each centroid of a cluster is a set of eigenvalues ​​that define the generated group. Checking the centroid feature weights can qualitatively explain what type of group each cluster represents.
We import the K-Means model from the sklearn library, fit the features, and make predictions.
K Means implementation in Python:
Hierarchical clustering
As the name suggests, hierarchical clustering is an algorithm that builds a hierarchical structure of clusters. The algorithm starts with all the data for a cluster allocated to them, and then joins the nearest two clusters to the same cluster. Finally, when there is only one cluster left, the algorithm ends.
The completion of hierarchical clustering can be represented using a tree diagram. The following is an example of hierarchical clustering. Datasets can be found here: https://raw.githubusercontent.com/vihar/unsupervised-learning-with-python/master/seeds-less-rows.csv
Hierarchical clustering implementation in Python:
Difference between K Means Clustering and Hierarchical Clustering
Hierarchical clustering does not handle big data well, but K Means clustering can. Because the time complexity of K Means is linear, ie O(n), the time complexity of hierarchical clustering is quadratic, ie O(n2).
In K Means clustering, when we start with arbitrary selection of clusters, the results of running the algorithm multiple times may be different. However, the results can be reproduced in hierarchical clustering.
When the shape of the cluster is a hypersphere (such as a circle in 2D, a sphere in 3D), K Means clustering is better.
K-Means clustering does not allow noisy data, but in hierarchical clustering, clustering can be done directly using noisy data sets.
t-SNE clustering
t-SNE clustering is one of unsupervised learning methods for visualization. t-SNE denotes the random neighbor embedding of the t-distribution. It maps high dimensional space to 2 or 3 dimensional space that can be visualized. Specifically, it models each high-dimensional object with two-dimensional points or three-dimensional points, such that similar objects are modeled by nearby points, and dissimilar objects are modeled by distant points with a great probability.
The t-SNE clustering implementation in Python, the dataset is the Iris dataset:
Here the Iris dataset has four features (4d) that are transformed and represented in a two-dimensional graph. Similarly, the t-SNE model can be applied to a data set with n features.
DBSCAN clustering
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular clustering algorithm that is used as an alternative to K-means in predictive analysis. It does not require the value of the input cluster to run. But as an exchange, you must adjust the other two parameters.
The scikit-learn implementation provides default values ​​for the eps and min_samples parameters, but these parameters usually need to be adjusted. The eps parameter is the maximum distance between two data points considered in the same neighborhood. The min_samples parameter is the minimum amount of data points in the neighborhood that are considered clusters.
DBSCAN clustering in Python:
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