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Introduction to Image Segmentation with K-Means clustering

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Image segmentation is an important step in image processing, and it seems everywhere if we want to analyze what's inside the image. For example, if we seek to find if there is a chair or person inside an indoor image, we may need image segmentation to separate objects and analyze each object individually to check what it is. Image segmentation usually serves as the pre-processing before pattern recognition, feature extraction, and compression of the image. Image segmentation is the classification of an image into different groups. Many kinds of research have been done in the area of image segmentation using clustering.


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.


Introduction to Image Segmentation with K-Means clustering

#artificialintelligence

Image segmentation is an important step in image processing, and it seems everywhere if we want to analyze what's inside the image. Many kinds of research have been done in the area of image segmentation using clustering. There are different methods and one of the most popular methods is K-Means clustering algorithm. Image segmentation is an important step in image processing, and it seems everywhere if we want to analyze what's inside the image. For example, if we seek to find if there is a chair or person inside an indoor image, we may need image segmentation to separate objects and analyze each object individually to check what it is.


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.


K-means for Beginners: How to Build from Scratch in Python

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The K-means algorithm is a method for dividing a set of data points into distinct clusters, or groups, based on similar attributes. It is an unsupervised learning algorithm which means it does not require labeled data in order to find patterns in the dataset. K-means is an approachable introduction to clustering for developers and data scientists interested in machine learning. In this article, you will learning how to implement k-means entirely from scratch and gain a strong understanding of the k-means algorithm. The goal of clustering is to divide items into groups such that objects in a group are more similar than those outside the group.