Unsupervised learning is a type of Machine Learning that involves discovering patterns in data without the use of labeled data. Unlike supervised learning, which uses labeled data to train a model, unsupervised learning relies on the model to find patterns and structures in the data on its own. This type of learning is mainly used for tasks such as clustering and dimensionality reduction.
Clustering is the process of grouping similar data points together. Clustering algorithms such as k-means and hierarchical clustering are used to find natural groupings in the data. This technique is used in a wide range of applications, such as market segmentation, anomaly detection, and image segmentation.
Dimensionality reduction is the process of reducing the number of features in the data while preserving as much of the information as possible. This technique is used to reduce the complexity of the data and make it easier to visualize and understand. Techniques like Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) are commonly used for dimensionality reduction.
Another important unsupervised learning technique is anomaly detection, which is used to identify unusual observations in the data, such as fraud detection and medical diagnosis.
In conclusion, Unsupervised learning is a type of Machine Learning that involves discovering patterns in data without the use of labeled data. It's mainly used for tasks such as clustering and dimensionality reduction. Clustering algorithms are used to find natural groupings in the data, and dimensionality reduction is used to reduce the complexity of the data and make it easier to visualize and understand.
Anomaly detection is used to identify unusual observations in the data, and it is an important technique for fraud detection and medical diagnosis. Unsupervised learning is becoming increasingly important as the amount of data available continues to grow, and it is likely to play an increasingly important role in the future of Machine Learning.
Common unsupervised learning tasks include clustering and dimensionality reduction.
Here is an example of unsupervised learning in Python using the scikit-learn library:
In this example, we are using the same iris dataset as in the previous example, but this time we're not using it for classification. We're using it for unsupervised learning, specifically to group similar samples together. First, we use the dimensionality reduction technique PCA to reduce the number of features to 2, then we use the K-Means clustering algorithm to group the samples into 3 clusters. The model is fit on the data, and the cluster labels are obtained by calling the labels_ attribute of the model.
Another unsupervised example is using of Association Rules
In this example, we're using the apriori algorithm from the mlxtend library to find frequent itemsets and association rules in a dataset of transactions. The data is defined as a list of lists, where each list represents a transaction. The min_support parameter is set to 0.5, meaning that an itemset must be present in at least 50% of the transactions to be considered frequent. The association_rules function is used to generate the association rules, with the metric set to confidence and the min_threshold set to 0.
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