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Exploring Clustering Algorithms: Explanation and Use Cases - neptune.ai

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Clustering (cluster analysis) is grouping objects based on similarities. Clustering can be used in many areas, including machine learning, computer graphics, pattern recognition, image analysis, information retrieval, bioinformatics, and data compression. Clusters are a tricky concept, which is why there are so many different clustering algorithms. Different cluster models are employed, and for each of these cluster models, different algorithms can be given. Clusters found by one clustering algorithm will definitely be different from clusters found by a different algorithm. Grouping an unlabelled example is called clustering. As the samples are unlabelled, clustering relies on unsupervised machine learning. If the examples are labeled, then it becomes classification. Knowledge of cluster models is fundamental if you want to understand the differences between various cluster algorithms, and in this article, we're going to explore this topic in depth.


Mastering Clustering with a Segmentation Problem - KDnuggets

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In the current age, the availability of granular data for a large pool of customers/products and technological capability to handle petabytes of data efficiently is growing rapidly. Due to this, it's now possible to come up with very strategic and meaningful clusters for effective targeting. And identifying the target segments requires a robust segmentation exercise. In this blog, we will be discussing the most popular algorithms for unsupervised clustering algorithms and how to implement them in python. In this blog, we will be working with clickstream data from an online store offering clothing for pregnant women.


Evaluating and Validating Cluster Results

arXiv.org Machine Learning

Clustering is the technique to partition data according to their characteristics. Data that are similar in nature belong to the same cluster [1]. There are two types of evaluation methods to evaluate clustering quality. One is an external evaluation where the truth labels in the data sets are known in advance and the other is internal evaluation in which the evaluation is done with data set itself without true labels. In this paper, both external evaluation and internal evaluation are performed on the cluster results of the IRIS dataset. In the case of external evaluation Homogeneity, Correctness and V-measure scores are calculated for the dataset. For internal performance measures, the Silhouette Index and Sum of Square Errors are used. These internal performance measures along with the dendrogram (graphical tool from hierarchical Clustering) are used first to validate the number of clusters. Finally, as a statistical tool, we used the frequency distribution method to compare and provide a visual representation of the distribution of observations within a clustering result and the original data.


Top 5 Machine Learning Algorithms used by Data Scientists with Python: Part-1

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Machine learning is an important Artificial Intelligence technique that can perform a task effectively by learning through experience. According to Forbes, machine learning will replace 25% of the jobs within the next 10 years. One of the most popular real-world applications of Machine Learning is classification. It corresponds to a task that occurs commonly in everyday life. For example, a hospital may want to classify medical patients into those who are at high, medium or low risk of acquiring a certain illness, an opinion polling company may wish to classify people interviewed into those who are likely to vote for each of several political parties or are undecided, or we may wish to classify a student project as distinction, merit, pass or fail.


Unsupervised learning explained

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Despite the success of supervised machine learning and deep learning, there's a school of thought that says that unsupervised learning has even greater potential. The learning of a supervised learning system is limited by its training; i.e., a supervised learning system can learn only those tasks that it's trained for. By contrast, an unsupervised system could theoretically achieve "artificial general intelligence," meaning the ability to learn any task a human can learn. If the biggest problem with supervised learning is the expense of labeling the training data, the biggest problem with unsupervised learning (where the data is not labeled) is that it often doesn't work very well. Nevertheless, unsupervised learning does have its uses: It can sometimes be good for reducing the dimensionality of a data set, exploring the pattern and structure of the data, finding groups of similar objects, and detecting outliers and other noise in the data.