In this post we will discuss about unsupervised learning as a sub-category of Machine Learning.
Machine learning is broadly categorised in to three types namely supervised, unsupervised & reinforcement learning. Here we will discuss about unsupervised learning, which represents a bunch of algorithms in which the targets are not necessarily needed. The model learns from unlabelled data without any explicit guidance or predefined outcomes. In this approach, the algorithm explores the data and tries to find patterns, structures, or relationships on its own in order to group the given data. Unsupervised learning is valuable when the target labels are not present. These techniques have proven to be quite effective in exploratory data analysis as they uncovering hidden structures, and gaining insights without the need for human-labeled annotations. However, evaluating the performance of unsupervised learning models can be more challenging compared to supervised learning, as there might not be clear metrics for assessing the quality of discovered patterns or structures. Clustering is a technique used in unsupervised machine learning, aimed at organizing a set of data points into groups or clusters based on their inherent similarities or patterns within the data.
Unsupervised Learning Industry Applications
The primary goal of unsupervised learning is to discover hidden patterns or intrinsic structures within the dataset. It involves tasks such as:
Clustering: It is the process of grouping similar data points together based on certain features or characteristics. Algorithms like K-means clustering or hierarchical clustering are commonly used for this purpose.
Dimensionality Reduction: At times the datasets are quite huge and processing them could turn out to be quite expensive, in such cases dimensionality reduction could be really helpful. These algorithms reduce the number of features in a dataset while preserving its essential information. Techniques like Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbour Embedding (t-SNE) fall under this category.
Anomaly Detection: Identifying unusual or abnormal instances in the dataset that differ significantly from the norm. This is crucial in various applications such as fraud detection or outlier analysis.
Association: Finding relationships and associations among different variables or items in a dataset. Apriori algorithm is an example used in market basket analysis to identify patterns in shopping behaviors.
Density Estimation: Estimating the probability distribution of the data, useful in generative modeling, anomaly detection, and understanding data distributions for statistical analysis.
Association Rule Learning: Discovering relationships or associations between items in datasets, often employed in market basket analysis, recommendation systems, and understanding consumer behavior.
Generative Modeling: Creating models that can generate new data resembling the input data's distribution. Applications include generating synthetic data for training models, image generation, and natural language processing.
Data Preprocessing: Unsupervised techniques can help in preprocessing steps, like imputing missing values, scaling features, or handling noisy data before supervised learning.
Market Segmentation: Given the historical market analysis data, these techniques could help in Identifying potential targets and grouping customers based on purchasing behaviour or demographics.
Image Segmentation: Partitioning an image into regions with similar characteristics or even identifying certain region from a given image. These techniques have proven to be very useful in classification and recognition based models.
Document Clustering: A large corpora of textual data could be organised into different known or unknown categories , may it be given topics or no categories at all.
Genomics: Clustering genes based on expression patterns for analysis.
These applications demonstrate the versatility of unsupervised learning across various industries, from finance and healthcare to marketing and cybersecurity. They enable data-driven insights and decision-making without the need for labeled data, making unsupervised learning a valuable tool in data analysis and pattern discovery. The goal is to partition the data points into groups where items in the same cluster are more similar to each other than to those in other clusters.
General Steps involved in Clustering
The process of clustering involves:
Data Representation: The data points are represented in a multidimensional space based on their features or attributes.
Similarity Measurement: A distance or similarity metric is used to quantify how close or similar data points are to each other. Common metrics include Euclidean distance, cosine similarity, or correlation coefficients, depending on the nature of the data.
Cluster Assignment: The algorithm iteratively assigns data points to clusters based on their similarity to other points within the cluster. Various algorithms like K-means, hierarchical clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models are commonly used for clustering.
Evaluation: After clustering, evaluation metrics such as silhouette score, Davies-Bouldin index, or within-cluster sum of squares can be used to assess the quality of the clusters formed.
List of Unsupervised Learning Algorithms
Unsupervised machine learning algorithms learn the patterns and different relationships between feature set itself. These kind of algorithms are defined by there use of unlabelled data. An unlabelled data is a dataset that contains a lot of examples of Features and Target for these features is not present. unsupervised learning uses algorithms that learn the structure, inside relationship and commonalities of Features from the dataset. This process is referred to as Training or Fitting. A bunch of such unsupervised learning algorithms are given below:
K-means clustering
KNN (k-nearest neighbours)
Hierarchal clustering
Anomaly detection
Neural Networks
Principle Component Analysis
Independent Component Analysis
Apriori algorithm
Singular value decomposition
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