According to the first page of Google results about 'texting K', society views receiving this message as akin to a one-letter insult. It's seen as something that we send when we're mad, frustrated, or otherwise want to put an end to a conversation. “K” is rude, dismissive, or cold.
When you send a “k,” what you're saying is: I don't care what you've said and I don't want to talk to you anymore.
K is a popular abbreviation for "OK," which in itself, is an abbreviation for "Okay." It is often used to answer in the affirmative to someone's question or comment. The abbreviation is used by all ages online and in text messages. It's a perfect option for those who think typing "OK" just takes too long.
It's seen as something that we send when we're mad, frustrated, or otherwise want to put an end to a conversation. “K” is rude, dismissive, or cold. While some of us might still view “K” unattached to a nefarious meaning, it appears that the vast majority of us are well aware of its reputation.
K means "Okay" and "Kids." The abbreviation K is typically used as a way of shortening the abbreviation "OK" (meaning "Okay") still further. As with "Okay," the use of K indicates acceptance, agreement, approval, or acknowledgment. However, it may sometimes be interpreted as lacking enthusiasm.
“k” is just a shorter way of saying “ok” which is a shorter way of saying “okay.” In texting, no one wants to use more letters, we want to use fewer letters. That's literally all it means.
K is the metric symbol for the prefix 'kilo'. This means 1,000 of whatever quantity you are measuring. So when you see this on websites, 1.79K likes means 1.79 thousand or 1790 likes.
The K in “10k followers” or “5K likes” comes from the metric system. It's the same K as the ones in these abbreviations: km (kilometer) kg (kilogram)
Advantages of k-means
Guarantees convergence. Can warm-start the positions of centroids. Easily adapts to new examples. Generalizes to clusters of different shapes and sizes, such as elliptical clusters.
In other words, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible. The 'means' in the K-means refers to averaging of the data; that is, finding the centroid.
Usually you don't have to respond at all. Whether you spell it okay, OK, 10-4, or just K, it means the same thing: it means the person you just spoke to heard you and agrees. It's a positive response. The great response to K is to say nothing and be happy that things are going okay.
The idea behind k-Means is that, we want to add k new points to the data we have. Each one of those points — called a Centroid — will be going around trying to center itself in the middle of one of the k clusters we have. Once those points stop moving, our clustering algorithm stops.
The k-Means Procedure
It can be viewed as a greedy algorithm for partitioning the n examples into k clusters so as to minimize the sum of the squared distances to the cluster centers.
There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster.
Yes you can use k-means to predict clusters. Once you have clustered your training data, you will receive cluster centers for the chosen number of clusters. E.g., if you have chosen k=3, your dataset will be divided into 3 clusters and hence you will receive 3 cluster centers.
But that's where we run into a problem: K-Means is slow when it comes to bigger datasets as there are just so many data points to compare. What's worse is that the most popular implementation of K-Means clustering, that of Scikit-Learn, isn't very well optimized.
K-means is only randomized in its starting centers. Once the initial candidate centers are determined, it is deterministic after that point. Depending on your implementation of kmeans the centers can be chosen the same each time, similar each time, or completely random each time.
We have proved that stability of K-means clustering is characterized by the geometry of the class HK with respect to P. It is evident that the choice of K maximizing stability of clustering aims to choose K for which there is a unique minimizer.
Real-life examples include spam detection, sentiment analysis, scorecard prediction of exams, etc. 2) Regression Models – Regression models are used for problems where the output variable is a real value such as a unique number, dollars, salary, weight or pressure, for example.
If you have reason to expect that your data has irregularly shaped or sized clusters, you should avoid using k-means clustering. If it is reasonable to assume the clusters will be ellipsoidal, you can use gaussian mixture models instead.
Convergence of K-means algorithm
After all objects have been assigned, the K centroids are updated. In doing so, the centroids move closer towards the set of objects that were already their own, thereby further reducing the distance of each point from its centroid.