Goto

Collaborating Authors

Clustering


Customer Segmentation using K-Means Clustering

#artificialintelligence

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. "The aim of marketing is to know and understand the customer so well the product or service fits him and sells itself".


Clustering : The Craft of Segmentation

#artificialintelligence

Clustering is the unsupervised learning process of segmenting observations, or dataset into number of groups such that data points have more similarity within the groups than those to the data points in other groups. It is one of the most frequently used analytical process. It helps tremendously in finding of homogeneous groups across the dataset. Clustering algorithms uses different measures to check similarity in order to cluster the observations. The type of similarity measure plays an important role in the final cluster formation.


Top 10 Research and Thesis Topics for ML Projects in 2022

#artificialintelligence

In this tech-driven world, selecting research and thesis topics in machine learning projects is the first choice of masters and Doctorate scholars. Selecting and working on a thesis topic in machine learning is not an easy task as machine learning uses statistical algorithms to make computers work in a certain way without being explicitly programmed. Achieving mastery over machine learning (ML) is becoming increasingly crucial for all the students in this field. Both artificial intelligence and machine learning complement each other. So, if you are a beginner, the best thing you can do is work on some ML projects.


DBScan Clustering Algorithm

#artificialintelligence

Clustering is an important topic in busyness, because it helps us to reduce the number of features to some typology, to some clusters which, in a case that data allows us, can give us more informations about our topic of interest. In a data science literature it is usually presented as dimension reduction technique, but in science, or even in data science it could reveal some additional pattern in data that is not obvious at the first glance. Imagine you have some features about some students: their marks, their personality traits, their ability scores, their motivation. Clustering could reveal you the completely new types of (un)successful students (it could be someone with high ability and low motivation -- underachiever, but at the same time it could be someone with high motivation and really good marks, but low abilities -- overachiever). This could simply done by clustering, while our cluster names (overachiever, underachiever) are basically interpretations of the clusters.


K-means clustering

#artificialintelligence

The topic I will try to explain today is K-means clustering. First, you might be wondering what the "K" means. K is a parameter that corresponds to the number of clusters you are trying to detect. For example, in order to detect 3 clusters like on the image on top, you would need to use K 3. But what does it mean?


A step-by-step guide for clustering images

#artificialintelligence

With unsupervised clustering, we aim to determine "natural" or "data-driven" groups in the data without using apriori knowledge about labels or categories. The challenge of using different unsupervised clustering methods is that it will result in different partitioning of the samples and thus different groupings since each method implicitly impose a structure on the data. Thus the question arises; What is a "good" clustering? Figure 2A depicts a bunch of samples in a 2-dimensional space. Intuitively we may describe it as a group of samples (aka the images) that are cluttered together. I would state that there are two clusters without using any label information.


Complete Machine Learning & Data Science with Python

#artificialintelligence

Machine learning is constantly being applied to new industries. Learn Machine Learning with Hands-On Examples What is Machine Learning? Machine Learning Terminology What are Classification vs Regression? Evaluating Performance-Classification Error Metrics Evaluating Performance-Regression Error Metrics Cross Validation and Bias Variance Trade-Off Use matplotlib and seaborn for data visualizations Machine Learning with SciKit Learn Linear Regression Algorithm Logistic Regresion Algorithm K Nearest Neighbors Algorithm Decision Trees And Random Forest Algorithm Support Vector Machine Algorithm Unsupervised Learning K Means Clustering Algorithm Hierarchical Clustering Algorithm Principal Component Analysis (PCA) Recommender System Algorithm Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective. Python is a general-purpose, object-oriented, high-level programming language. Python is a multi-paradigm language, which means that it supports many programming approaches. Along with procedural and functional programming styles Python is a widely used, general-purpose programming language, but it has some limitations. Because Python is an interpreted, dynamically typed language Python is a general programming language used widely across many industries and platforms. One common use of Python is scripting, which means automating tasks. Python is a popular language that is used across many industries and in many programming disciplines. DevOps engineers use Python to script website. Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar Machine learning describes systems that make predictions using a model trained on real-world data. Machine learning is being applied to virtually every field today. That includes medical diagnoses, facial recognition, weather forecasts, image processing.


10 Popular Machine Learning Algorithms In A Nutshell

#artificialintelligence

Hierarchical clustering means creating a tree of clusters by iteratively grouping or separating data points. There are two types of hierarchical clustering named Agglomerative clustering and Divisive clustering. Agglomerative clustering is the bottom-up approach. It merges the two points that are the most similar until all points have been merged into a single cluster. Divisive clustering is the top-down approach.


Understanding your performance metrics for clustering

#artificialintelligence

Clustering is categorized under unsupervised learning, which forms the niche part of machine learning. Unlike supervised learning which is more common in most common machine learning study, classification tasks learn from the provided labeled data and makes class predictions. However, this does not cause the clustering method to be less desirable, as clustering algorithms are essential in discovering unexplored insights. Thus, it is important to understand the performance of the clustering task and to decide whether the clusters formed are trustable. Silhouette Analysis is the most common method as it is more straightforward compared to others.


A Note on Machine Learning

#artificialintelligence

Unsupervised learning uses algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns and data groupings without the need of human intenvention. Generally it's used for expolarity data analysis, customer segmentation, recommender systems, big data visualization, feature elicitation etc. Roughly, there are three types of unsupervised learning approach. Clustering is a data mining techinuqe which groups unlabeled data based on similarities and differences. Clustering algorithms are used to process raw, unclassified data objects into groups represented by structures and patterns in the information.