Description:
The term ‘Unsupervised’ refers to the fact that the algorithm is not guided like a supervised learning algorithm. Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. It is used in many applications, such as clustering, anomaly detection, and recommender systems.
Unsupervised learning can discover hidden patterns and features in data that are not immediately apparent. It can also help with exploratory data analysis and feature selection. It is particularly useful for tasks that do not have a clearly defined output.
Unsupervised algorithms are used to analyze data and identify patterns or trends in the data that would otherwise be difficult to uncover. They are used in a wide range of applications, including customer segmentation, fraud detection, financial forecasting, medical diagnosis, and more.
The unsupervised algorithm works with unlabeled data. It relies on algorithms and techniques like clustering, association rule mining, anomaly detection and neural network models.
An Unsupervised Machine Learning Algorithm is used to:
- Explore the structure of the information.
- Extract valuable insights.
- Detect patterns.
- Implement this into its operation in order to increase efficiency.
Unsupervised learning applies two major techniques viz. clustering and dimensionality reduction.
Clustering:
Clustering is the process of grouping similar data points together and is used for segmentation, recommendation systems, market analysis, and image segmentation. Dimensionality reduction is the process of reducing the number of random variables under consideration and is used for feature extraction, data compression, and visualization.
K-means clustering:
K-means clustering is an unsupervised machine learning algorithm used to create clusters of similar data points. It works by minimizing the sum of squared distances between each data point and its assigned cluster center. The algorithm begins by randomly assigning each data point to a cluster, then iteratively adjusts the cluster centers by computing the mean of all of the points in that cluster. The process is repeated until the clusters are stable and can no longer be further optimized.
K-means clustering is also used in the following operations:
- Audience segmentation
- Customer persona investigation
- Anomaly detection
- Pattern recognition
- Inventory management
K-Means clustering data algorithm:
Step 1. Import dependent libraries.
Step 2. Import data set of people’s income in panda dataframe.
Output:
Step 3. Read Bengaluru house data CSV file in panda framework.
Step 4. According to the scatter plot b/w age and income we analyze in this data have three clusters. Fit the data in 3 kmean clusters.
Output:
Step 5. Make three different data sets according to the cluster. Plot these data in scatter form.
Note: In this plot, you can see that cluster 2 points are scattered with centroid but cluster 1 and cluster 3 are not scattered with centroid because x and y scale is not proper.
Step 6. Now, scale age and income data sets write ith MinMaxScaler() function for scale each value of the dataset.
Step 7. Again predict clusters.
Step 8. The scale is set so we plot the data b/w age and income data.
Output:
Conclusion:
K-Means clustering is a powerful and simple data clustering algorithm that can quickly discover meaningful structures in large amounts of data. This tutorial has provided an overview of the K-Means algorithm and how to implement it using Python. We discussed the concept of clustering, and the K-Means algorithm and used the K-Means algorithm to cluster a dataset of points into two clusters. Finally, we discussed how to evaluate the performance of the K-Means algorithm. With this knowledge, you are now ready to use the K-Means algorithm to uncover meaningful insights from your own data.
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