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Clustering


Advanced K-Means: Controlling Groups Sizes and Selecting Features

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The algorithm uses ideas from Linear Programming, in particular Network Models. Networks models are used, among other things, in logistics to optimise the flow of goods across a network of roads. We can see in the simple figure above that we have 5 nodes with directed arcs (the arrows) between them. Each node has a demand (negative) or supply (positive) value and the arcs have flow and cost values. For instance, the arc 2–4 has a flow of 4 and a cost of $2. Similarly, node 1 supplies 20 units and node 4 requires 5 units.


20 Data Science Interview Questions for a Beginner

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Success is a process not an event. Data Science is growing rapidly in all sectors. With the availability of so many technologies within the Data Science domain, it becomes tricky to crack any Data Science interview. In this article, we have tried to cover the most common Data Science interview questions asked by recruiters. Answer: The question can also be phrased as to why linear regression is not a very effective algorithm.


The Best 5 Courses in this Specialization

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This Specialization 160,486 recent views The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.


Colour Quantization Using K-Means Clustering and OpenCV

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Have you ever wondered how we can implement a machine learning algorithm on the pixel intensity value with a common K-means clustering algorithm? In this method, we would generate a compressed variant of our picture with more scattered colours. The image will be processed in a lower intensity resolution, whereas the fraction of pixels will prevail. This procedure is very interesting, so I expect that you will like it. This article can appear as a particularly impressive and unexpected one, so here is the link to the article, please have a read and hope you like it.


Clustering in Machine Learning - GeeksforGeeks

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It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. It is not necessary for clusters to be a spherical.


Foundations of Data Science: K-Means Clustering in Python

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This Course Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government. This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary for typical data analysis tasks. You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques.


All machine learning models explained

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As a beginner, you want to know what are the models and algorithms available in machine learning that make our work more easier. Supervised learning involves learning a function that maps an input to an output based on example input-output pairs. In regression models, the output is continuous. The idea of linear regression is simply finding a line that best fits the data. Extensions of linear regression include multiple linear regression.


Everything on Hierarchical Clustering

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In this article, you will learn. Clustering is the most common form of unsupervised learning on unlabeled data to clusters objects with common characteristics into discrete clusters based on a distance measure. Hierarchical Clustering is either bottom-up, referred to as Agglomerative clustering, or Divisive, which uses a top-down approach. A bottom-up approach where each data point is considered a singleton cluster at the start, clusters are iteratively merged based on similarity until all data points have merged into one cluster. Agglomerative clustering agglomerates pairs of clusters based on maximum similarity calculated using distance metrics to obtain a new cluster, thus reducing the number of clusters with every iteration.


K-Means Clustering: Techniques to Find the Optimal Clusters

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As the points are uniformly distributed, the KMeans algorithm evenly splits the points into K clusters even if there's no separation between them Gap Statistics gives the optimal number of the cluster as 10 based on the maximum gap between the cluster inertia on the data and null referenced data.


Fine grained analysis of K- mean clustering and where we are using it

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You can transform data for multiple features to the same scale by normalizing the data. In particular, normalization is well-suited to processing the most common data distribution, the Gaussian distribution. Compared to quantiles, normalization requires significantly less data to calculate.