"Clustering: taking an automatic grouping of similar objects into sets."
"There are a few use cases where you definitely want to use clustering."
"You can create a highly available kind of cluster using this replication mechanism."
"So as you see here the Fisher Jenks estimate this k-means solution is slightly better on homogeneity and separation then is the agglomerative clustering method."
"Now let's have a look which went where so you could see the cluster 0 which has sentences like the girl is a crying baby oh sorry the girl is carrying baby sorry then the baby is carried by the woman right."
"Clustering is basically where the machine forms groups based on the behavior of the data."
"UMAP is popular because it is relatively fast even with large datasets, and similar samples tend to cluster together in the final output, so it is useful for identifying similarities and outliers. Bam!"
"If you need to choose a good clustering key, if you don't need a clustering key, avoid it."
"K-means is a very popular clustering algorithm."
"Hierarchical clustering turns your clusters into a tree."
"Word embeddings are like this huge nexus of words that have semantically similar words forming clusters, built with the help of a complex algorithm that establishes semantic relationships among words based on their usage in millions of sentences."
"WGCNA by default has a much more sophisticated and iterative process for identifying clusters."
"Topic modeling is a way that we use unsupervised learning in machine learning terms to throw a whole bunch of documents at a model so that it can find patterns in those documents and cluster them into topics."
"Density based clustering is going to allow your data to define the shape of your clusters."
"The bees now are beginning to cluster."
"Clustering is a technique where similar objects are put together."
"Community detection... we're interested in finding clusters or partitions in the graph."
"K-means is a very popular algorithm."
"The consistent hashing-based clustering is really efficient."
"Positive spatial autocorrelation is clumps of like values clustering."
"What if clusters overlap? How do we deal with some of these? Apparently, it looks like Gaussian mixture model works very well in these type of scenarios."
"The first thing you want to do is specify the number of clusters you want, which is what 'k' means."
"The precision is actually enhanced when we have clustered data because the clustering is taken into account."
"That should create clusters in this multi-dimensional space."
"We want to try and do is reframe our data, maybe move it around so that we can better separate things out, better cluster things, perhaps it's better for machine learning."
"Clustering has some very interesting applications in domains such as genetics or evolutionary biology."
"Unsupervised machine learning is like when you have a bunch of objects and what you're trying to do is put those into groups that are similar in some way."
"Clustering is really critical to the future of high availability and fast recovery."
"The ability to cluster nodes together to give you this high availability... ultimately made elastic so popular in the first place."
"By far the most famous is K-means, so that's where I'm going to start."
"Density based clustering is a particular flavor or family of methods that's sometimes a good alternative."
"We can recover much more complex cluster shapes with density based clustering."
"The core points are the high density points at the center of clusters."
"Level set trees describe the entire hierarchy of the density-based clusters."
"Clustering is often one of the first things that we do as humans when we start working with a new data set to get some sense of the structure of that data."
"You can make use of clustering by grouping the places into four clusters."
"Clustering can group the data into different clusters and discover some patterns."
"K-means analysis is based on one of the simplest algorithms for solving the cluster problem."
"These networks are scale-free, have small diameter and average path lengths, and high clustering coefficient."
"The main goal is to form groups or clusters of similar records."
"Interpretation: Obtain meaningful and useful clusters."
"The general idea is that this method will help us group companies in a way that they represent similar qualities."
"Classification predicts a class, regression predicts a number, and unsupervised learning does not have a Y variable; it's just clustering or segmenting your data based on patterns."
"We're going to look at trying to cluster the passes into groups."
"We're going to run k-means for 25 clusters, for 50 clusters, or for 75 clusters."
"K-means is an unsupervised learning method which means we have unlabeled data that clusters a dataset into k different clusters."
"The means, also called centroids, and clusters are updated during an iterative optimization process."
"We update the cluster labels by assigning the points to the nearest cluster center."
"Docker Swarm is a service for containers which allows IT administrators and developers to create and manage a cluster of swarm nodes."
"The dark matter... it's thought that the dark matter for basically because of this tendency to cluster is cold."
"This kind of work, which was great, managed to correctly identify three clusters that made sense."
"With availability groups, we decided, hey, why don't we use OS clustering because they know how to handle virtual names."
"It's not perfect separation, but you actually get pretty decent clustering."
"The ICC is indicating that we have evidence of clustering and really fairly substantial clustering going on in our data."
"With this you can get a bit more control over how exactly you want the clustering to happen very easily."
"Harmony uses an iterative clustering method which ensures that cells in each cluster come from as many batches as possible."
"Clustering is the most popular type of unsupervised learning."
"Clustering is useful for many applications, for example, it can be used to automatically organize data."
"Look at that, that's beautiful. There's like three clusters, excellent."
"Wow, that is beautiful separation."
"Clustering is a set of techniques for finding subgroups or clusters in a dataset."
"K-means clustering partitions the data into a specified number of clusters and assumes that each observation belongs to at least one of the clusters."
"The index that's been computed as Moran's index tells us it's a positive number which tells us that there is apparent clustering of like values near each other."
"The maximum peak is at 900 ft so 900 ft seems to be the sweet spot that's the point at which you find maximum clustering."
"You have a set of data and you want to group them into clusters."
"The simplest method for clustering is known as the K-means clustering algorithm."
"Clustering techniques aim to group similar instances into clusters, which is an important class of unsupervised learning algorithms."
"The clusters do not overlap, and the variation within each cluster is minimized."
"Clustering... they give a whole week to clustering K-means, hierarchical, DBSCAN - great stuff."
"The idea of k-means is to minimize total within cluster Euclidean distance between all points and the cluster centroid."
"Hierarchical clustering... allows you to actually see that hierarchical structure."
"Clustering basically goes and finds groups of objects that are very similar."
"We just want to train a system that clusters together the pictures from the same person and puts far apart images that come from different persons."
"The clustering step is capturing the essential information which is what images go together and what images do not."
"This is incredibly useful if you have lots of variables because it'll give you a better feeling for why something became a cluster."
"You can set up a full cluster on Proxmox completely for free and have the full functionality of the OS."
"You can train a deep neural network in an unsupervised fashion to cluster those images based on the digit that is present."
"It provides us hierarchical communities, not just clustering at one level, but clustering of clusters."
"Using these three parameters, we are going to group the customers into a number of clusters and see in which cluster the majority of our customers fall in."
"K-means is an unsupervised machine learning algorithm used for clustering data points based on similarities."
"The elbow method is used to find the number of clusters we have, which is one of the most widely used ways to find the number of clusters."
"Entities with similar properties tend to cluster and agglomerate in certain network regions."
"Planet nine confines these distant orbits into a cluster."
"Kafka runs as a cluster on one or more servers that are called brokers."
"Divisive clustering begins with a whole set and proceeds to divide it into smaller clusters."
"It has a built-in high availability and clustering."
"Unsupervised machine learning is separated into two dichotomies: flat clustering and hierarchical clustering."
"What we're actually doing with k-means is clustering them based on equal variance from each other on the plane."
"Clustering is another machine learning technique which helps you identify the common patterns and groupings among the people that are on your website."
"Clustering is when you're trying to look at what are the groups in this data."
"We get three clusters of regions: with the highest sales, region with average sales, region with the lowest sales."
"You can use clustering to group those things together and then start going into those groups and see what makes up these groups."