Classification of Machine Learning-Part 2: Unsupervised Learning
Machine learning has become a popular method for enhancing the user experience and testing a system for assuring quality. The current article is the second of the serie of three articles on the classification of machine learning. Unsupervised machine learning means using Artificial Intelligence (AI) algorithms for identifying patterns within data consisting of data points that have not been classified or labeled before. The algorithms are utilized for labeling, classifying, or grouping these data points without any external guidance to perform the task. In unsupervised learning, the system identifies patterns existing in data sets on its own.
The Artificial Intelligence System is provided with uncategorized and unlabelled data on which algorithms of the system function without any training. The output depends on the coded algorithms. It is a common way to test the capability of that system. Unsupervised learning algorithms can perform more complicated tasks; however, it is unpredictable. The standard approaches of conducting unsupervised learning effectively are clustering, association, and dimensionality reduction. Clustering is a data mining technique that identifies patterns or structures in unlabelled raw data depending on their similarities and differences. These clustering algorithms can be classified into specifically exclusive, hierarchical, overlapping, and probabilistic.
Association is a method based on a rule for identifying relations between variables in a data set. These strategies are utilized frequently for market-based analysis, thus allowing the organizations to comprehend the association between various products better. The algorithms used for generating association rules include Apriori, FP-Growth, and Eclat, with Apriori being widely used. Dimensionality reduction is used when the data set consists of a higher number of dimensions or features. It helps in reducing the input to a manageable size and preserves the integrity of the data set. The methods used for dimensionality reduction include singular value decomposition, principal component analysis, and encoders.
Unsupervised learning helps in anomaly detection by identifying unusual data points. This is used to investigate fraudulent transactions, human error, or faulty hardware pieces. Association mining can identify items that reoccur in a data set. The pre-processing of data is carried out by latent variable models, reducing the features or decomposing data into various components. Google News uses unsupervised learning for categorizing articles on the same story from different outlets of online news. It also provides essential features like image detection, segmentation to medical imaging devices used in pathology and radiology for quick and accurate diagnosis of patients.
Unsupervised learning allows machines to identify problems that may seem impossible for humans. Data scientists can use this as unsupervised learning helps in understanding raw data. It resembles human intelligence in a way as the model learns gradually and then computes the result. However, unsupervised learning can not provide precise information about data classification as input data is not known. This leads to less accuracy in results as the machine has to process the data on its own.
Final words
Unsupervised learning can be used when data on desired outcomes is not available. The primary purpose of unsupervised learning is identifying hidden patterns and data in a data. It can help in detecting anomalies like fraudulent transactions. It is pretty useful; however, the most significant drawback is that it cannot provide accurate information about data classification as machine functions on its own.
References
https://www.guru99.com/unsupervised-machine-learning.html
https://www.ibm.com/cloud/learn/unsupervised-learning
https://www.datarobot.com/wiki/unsupervised-machine-learning/
https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/
https://searchenterpriseai.techtarget.com/definition/unsupervised-learning
https://www.upgrad.com/blog/how-does-unsupervised-machine-learning-work/
