# What does k mean in numbers

K-Means Clustering Algorithm

24 rows · Sep 17, · Kilo is a decimal unit prefix in the metric system denoting multiplication by . Feb 01, · it means thousand! 1k= one thousand (1,) 10k = ten thousand (10,) k= thousand (,).

We are given a data set of items, with certain features, and values for these features like a vector. The task what plants do dragonflies eat to categorize those items into groups. To achieve this, we will use the kMeans algorithm; an unsupervised learning algorithm. Overview It will help if you think of items as points in an n-dimensional space.

The algorithm will categorize the items into k groups of similarity. To calculate that similarity, we will use the euclidean distance as measurement. The algorithm works as follows: First we initialize k points, called means, randomly.

We repeat the process for a given number of iterations and at the end, we have our clusters. To initialize these means, we have a lot of options. An intuitive method is to initialize the means at random items in the data set.

Another method is to initialize the means at random values between the boundaries of the data set if for a feature x the items have values in [0,3], we will initialize the means with values for x at [0,3]. Each line represents an item, and it contains numerical values one for each feature split by commas. You can find a sample data set here.

We will read the data from the file, saving it into a list. Each element of the list is another list containing the item values for the features. For that, we need to find the min and max for each feature. Skip to content. Related Articles. Read the what zodiac sign is gemini, splitting by lines. Convert feature value to float. Add feature value to dict. Initialize means to random numbers between.

Set value to a random float. The square root of the sum. Classify item to the mean with minimum distance. Find distance from item to mean.

Find the minima and maxima for columns. Initialize means at random points. Initialize clusters, the array to hold. An array to hold the cluster an item is in. Calculate means. If no change of cluster occurs, halt. Classify item into a cluster and update the. Item changed cluster. Nothing changed, return. Classify item into a cluster. Add item to cluster.

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K comes form the Greek kilo which means a thousand. In the metric system lower case k designates kilo as in kg for kilogram, a thousand grams. Even here there is some ambiguity. In the language of computer science K is 2 10 = ‘k’ comes from Greek Kilo (or) Greek word chilioi or khilioi which means thousand. Its written in lower case ‘k’ when representing money. ‘’ is numeric number and followed by lower ‘k’ means multiply it by thousand. Feb 01, · it means thousand! 1k= one thousand (1,) 10k = ten thousand (10,) k= thousand (,).

Sign in. You want to know if that data categorization makes sense or not, or can be improved. Well, my advice is that you cluster your data. Information is often darkened by noise and redundancy, and grouping data into clusters clustering with similar features is an efficient way to bring some light on. C l ustering is a technique widely used to find groups of observations called clusters that share similar characteristics.

The result is that observations or data points in the same group are more similar between them than other observations in another group.

The goal is to obtain data points in the same group as similar as possible, and data points in different groups as dissimilar as possible. Extremely well fitted for exploratory analysis, K-means is perfect for getting to know your data and providing insights on almost all datatypes.

Whether it is an image, a figure or a piece of text, K-means is so flexible it can take almost everything. Clustering including K-means clustering is an unsupervised learning technique used for data classification. Unsupervised learning means there is no output variable to guide the learning process no this or that, no right or wrong and data is explored by algorithms to find patterns.

We only observe the features but have no established measurements of the outcomes since we want to find them out. Within the universe of clustering techniques, K-means is probably one of the mostly known and frequently used.

K-means uses an iterative refinement method to produce its final clustering based on the number of clusters defined by the user represented by the variable K and the dataset. For example, if you set K equal to 3 then your dataset will be grouped in 3 clusters, if you set K equal to 4 you will group the data in 4 clusters, and so on.

K-means starts off with arbitrarily chosen data points as proposed means of the data groups, and iteratively recalculates new means in order to converge to a final clustering of the data points. But how does the algorithm decide how to group the data if you are just providing a value K? A centroid is a data point that represents the center of the cluster the mean , and it might not necessarily be a member of the dataset. This is how the algorithm works:. The initial result of running this algorithm may not be the best possible outcome and rerunning it with different randomized starting centroids might provide a better performance different initial objects may produce different clustering results.

Forgy or Kaufman approaches. But another question arises: how do you know the correct value of K, or how many centroids to create? There is no universal answer for this, and although the optimal number of centroids or clusters is not known a priori, different approaches exist to try to estimate it.

One commonly used approach is testing different numbers of clusters and measure the resulting sum of squared errors, choosing the K value at which an increase will cause a very small decrease in the error sum, while a decrease will sharply increase the error sum. K-means is a must-have in your data science toolkit, and there are several reasons for this.

After all, you need to define just one parameter the value of K to see the results. It is also fast and works really well with large datasets , making it capable of dealing with the current huge volumes of data. Furthermore, the algorithm is so popular that you may find use cases and implementations in almost any discipline. Nevertheless, K-means presents some disadvantages.

The first one is that you need to define the number of clusters, and this decision can seriously affect the results. Also, as the location of the initial centroids is random, results may not be comparable and show lack of consistency.

Additionally, it assumes that data points in each cluster are modeled as located within a sphere around that cluster centroid spherical limitation , but when this condition or any of the previous ones is violated, the algorithm can behave in non-intuitive ways.

Example 1: On the left-hand side the intuitive clustering of the data, with a clear separation between two groups of data points in the shape of one small ring surrounded by a larger one.

On the right-hand side, the same data points clustered by K-means algorithm with a K value of 2 , where each centroid is represented with a diamond shape. As you see, the algorithm fails to identify the intuitive clustering. Example 2: On the left-hand side the clustering of two recognizable data groups.

On the right-hand side, the result of K-means clustering over the same data points does not fit the intuitive clustering. Example 3: Once again, on the left-hand side there are two clear clusters one small and tight data group and another larger and dispersed one which K-means fails to identify right-hand side. The thing is real life data is almost always complex, disorganized and noisy.

Situations in the real world rarely reflect clear conditions in which to apply these type of algorithms right out of the shelf. In the case of K-means algorithm it will be expected that at least one of its assumptions gets violated, so we need not only to identify this, but to know what to do in such case.

The good news is that there are alternatives, and deficiencies can be corrected. For example, converting data to polar coordinates can solve the spherical limitation we described in example 1. You may also consider using other types of clustering algorithms if you find serious limitations. Possible approaches would be to use density-based or hierarchical-based algorithms, which fix some of K-means limitations but have their own limitations.

In summary, K-means is a wonderful algorithm with lots of potential uses, so versatile it can be used for almost any kind of data grouping. Thanks Sabrina Steinert for your valuable inputs. Interested in these topics? Follow me on Linkedin or Twitter. Every Thursday, the Variable delivers the very best of Towards Data Science: from hands-on tutorials and cutting-edge research to original features you don't want to miss.

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