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K means pseudocode latex

We are given a data set of items, with certain features, and values for these features like a vector. To achieve this, we will use the kMeans algorithm; an unsupervised learning algorithm.

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. 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. We do this with the following function:.

For that, we need to find the min and max for each feature. We accomplish that with the following function:. The variables minima, maxima are lists containing the min and max values of the items respectively. We will be using the euclidean distance as a metric of similarity for our data set note: depending on your items, you can use another similarity metric.

K means Clustering – Introduction

We can do this by adding all the values and then dividing by the number of items, or we can use a more elegant solution.

We will calculate the new average without having to re-add all the values, by doing the following:. We do the above for each feature to get the new mean. For the given item, we will find its similarity to each mean, and we will classify the item to the closest one. We will repeat the process for some fixed number of iterations.

If between two iterations no item changes classification, we stop the process as the algorithm has found the optimal solution. The below function takes as input k the number of desired clustersthe items and the number of maximum iterations, and returns the means and the clusters. The classification of an item is stored in the array belongsTo and the number of items in a cluster is stored in clusterSizes.

Finally we want to find the clusters, given the means. We will iterate through all the items and we will classify each item to its closest cluster. Cosine distance: It determines the cosine of the angle between the point vectors of the two points in the n dimensional space.

Manhattan distance: It computes the sum of the absolute differences between the co-ordinates of the two data points. Minkowski distance: It is also known as the generalised distance metric. It can be used for both ordinal and quantitative variables. You can find the entire code on my GitHubalong with a sample data set and a plotting function. Thanks for reading. This article is contributed by Antonis Maronikolakis.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute. See your article appearing on the GeeksforGeeks main page and help other Geeks. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.Select k points at random as cluster centers.

Assign objects to their closest cluster center according to the Euclidean distance function. Calculate the centroid or mean of all objects in each cluster. Repeat steps 2, 3 and 4 until the same points are assigned to each cluster in consecutive rounds. K-Means is relatively an efficient method.

However, we need to specify the number of clusters, in advance and the final results are sensitive to initialization and often terminates at a local optimum. Unfortunately there is no global theoretical method to find the optimal number of clusters. A practical approach is to compare the outcomes of multiple runs with different k and choose the best one based on a predefined criterion.

In general, a large k probably decreases the error but increases the risk of overfitting. K-Means clustering intends to partition n objects into k clusters in which each object belongs to the cluster with the nearest mean. This method produces exactly k different clusters of greatest possible distinction. The best number of clusters k leading to the greatest separation distance is not known as a priori and must be computed from the data.

The objective of K-Means clustering is to minimize total intra-cluster variance, or, the squared error function:. Suppose we want to group the visitors to a website using just their age one-dimensional space as follows:.

Nearest Cluster. No change between iterations 3 and 4 has been noted. By using clustering, 2 groups have been identified and The initial choice of centroids can affect the output clusters, so the algorithm is often run multiple times with different starting conditions in order to get a fair view of what the clusters should be.

K Means Interactive.The reason we find that much importance is given to classification algorithms and not much is given to regression algorithms is because a lot of problems faced during our diurnal routine belongs to the classification task.

For example, we would like to know whether a tumour is malignant or benign, we like to know whether the product we sold was received positively or negatively by the consumers, etc. K nearest neighbours is another classification algorithm and it is very simple one too. In K means algorithm, for each test data point, we would be looking at the K nearest training data points and take the most frequently occurring classes and assign that class to the test data.

Therefore, K represents the number of training data points lying in proximity to the test data point which we are going to use to find the class. We will be using the Iris dataset to illustrate the KNN algorithm.

This helps us to better visualize the data points. We load the Iris dataset and obtain the target values and store it in an array. From plotting the data points based on the two features, we obtain a scatter plot as shown below. We convert the classes which is present in string types into integer types.

How to write algorithm and pseudocode in Latex ?\usepackage{algorithm},\usepackage{algorithmic}

We use Scikit-learn library to implement our KNN algorithm. We take a range of values of K, from 1 to 20 and plot the accuracy of our KNN model based on the different values of K. This would provide us with a better intuitive understanding of how the algorithm works. We implement the algorithm based on the pseudocode mentioned above.

K nearest neighbours is a simple classification algorithm with a wide array of applications. Knowing how it works definitely helps :.

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Sign in. Another classification algorithm. Rohith Gandhi Follow. Introduction The reason we find that much importance is given to classification algorithms and not much is given to regression algorithms is because a lot of problems faced during our diurnal routine belongs to the classification task.

What is K?? K Nearest Neighbours — Pseudocode 1. Load the training and test data 2. Choose the value of K 3. For each point in test data: - find the Euclidean distance to all training data points - store the Euclidean distances in a list and sort it - choose the first k points - assign a class to the test point based on the majority of classes present in the chosen points 4.

It only takes a minute to sign up. Here is an implementation of an algorithm in pseudocode using algorithm2e :. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Pseudocode Latex Ask Question. Asked 3 years, 3 months ago. Active 3 years, 3 months ago. Viewed 5k times. Can you show us what you've tried? Add a MWE, please. This seems to be a good starting point.

Featured on Meta. Community and Moderator guidelines for escalating issues via new response…. Feedback on Q2 Community Roadmap. Related 5. Hot Network Questions. Question feed.Say you are given a data set where each observed example has a set of features, but has no labels. Labels are an essential ingredient to a supervised algorithm like Support Vector Machines, which learns a hypothesis function to predict labels given features.

So we can't run supervised learning. What can we do? One of the most straightforward tasks we can perform on a data set without labels is to find groups of data in our dataset which are similar to one another -- what we call clusters. K-Means is one of the most popular "clustering" algorithms. A point is considered to be in a particular cluster if it is closer to that cluster's centroid than any other centroid. K-Means finds the best centroids by alternating between 1 assigning data points to clusters based on the current centroids 2 chosing centroids points which are the center of a cluster based on the current assignment of data points to clusters. Figure 1: K-means algorithm.

K means Clustering – Introduction

Training examples are shown as dots, and cluster centroids are shown as crosses. In each iteration, we assign each training example to the closest cluster centroid shown by "painting" the training examples the same color as the cluster centroid to which is assigned ; then we move each cluster centroid to the mean of the points assigned to it. Images courtesy of Michael Jordan.

The k-means clustering algorithm is as follows:. Here is pseudo-python code which runs k-means on a dataset.

What is pseudocode ? How is it useful in developing logic for the solution of a problem?

It is a short algorithm made longer by verbose commenting. Function: K Means K-Means is an algorithm that takes in a dataset and a constant k and returns k centroids which define clusters of data in the dataset which are similar to one another. Book keeping. K-means terminates either because it has run a maximum number of iterations OR the centroids stop changing. Make that centroid the element's label.

Function: Get Centroids Returns k random centroids, each of dimension n. Important: If a centroid is empty no points have that centroid's label you should randomly re-initialize it.

Important note: You might be tempted to calculate the distance between two points manually, by looping over values. This will work, but it will lead to a slow k-means! And a slow k-means will mean that you have to wait longer to test and debug your solution.

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To calculate the distance between all the length 5 vectors in z and x we can use: np. K-Means is really just the EM Expectation Maximization algorithm applied to a particular naive bayes model. Instead of storing this conditional probability as a table, we are going to store it as a single normal gaussian distribution, with it's own mean and a standard deviation of 1.Then you assign each data point to the cluster it's closest to.

You then take the average of each cluster and move each centroid center of each cluster to their averages. This process continues until convergence, successfully grouping the data and finding structure. Let's say you are an evil overlord and you want to use your mind controlling device to manipulate all of America.

Your device plays a loud sound and when people hear it, they become controlled by you. The further away someone is from your device, the weaker the signal. You have 5 copies of it. The most optimal way for you to control everyone is to have all 5 devices spread out across the country.

If all the devices are near each other, then only a small fraction of people will be controlled by a strong signal and a majority of people around the country will be controlled by a weak signal. So, you randomly place all 5 devices near the middle of the country initialization step. Each device activates and everyone data points in the country is controlled by the device closest to them assigning to cluster.

This is not optimal because people who live on the east and west coast are controlled by a weak signal which could possibly jeopardize your evil plan. So, for each device, we find the location of where it's average signal strength is at mean of cluster. We then relocate each device to that location placing centroid. We then activate the devices again, having everyone controlled by the device closest to them.

We repeat these steps until convergence. Once we reach convergence, we have the devices spread out across the people of the country, efficiently controlling all people with solid signals. Upon further investigation, we see that the devices have also grouped specific type of people. Each device has grouped people who all have properties in common, without us looking for it. We see that the people in device greenblueand orange are mostly affiliated with the democratic party and the people controlled by device purple and red are mostly affiliated with the republican party.

We have found a way to group similar people together without knowing what to look for. This is precisely what clustering is set out to do. Find structure, determining the intrinsic grouping in a set of unlabeled data. Most of the algorithms I will discuss are supervised learning algorithms, meaning that they are presented with a label during the training phase.

You take the data, with the label, and train your model to recognize patterns in the data that will lead to their respective labels. K-Means clustering, however, is an unsupervised algorithm, meaning there is no label in the training data. The purpose is to find hidden patterns or groupings in the data.

For example, if you had dogs and knew all of their features size, bark level, color, etc. At the end of the clustering, all the dobermans, pit bulls, and corgis would be clustered into 3 different groups, even if you didn't know that's what they were called.In machine learning, we are not always provided an objective to optimize, we are not always provided a target label to classify the input data points into. The kinds of problems where we are not provided with an objective or label to classify is termed as an unsupervised learning problem in the domain of AI.

In an unsupervised learning problem, we try to model the latent structured information present within the data. K-means algorithm is a famous clustering algorithm that is ubiquitously used. K represents the number of clusters we are going to classify our data points into.

The simulations below would provide a better understanding of the K-means algorithm. There would be some instances where we would not know the number of clusters. Then, how can we choose the value for K??? There is a way called the elbow method.

K Means Clustering Algorithm - K Means Example in Python - Machine Learning Algorithms - Edureka

In this method, you choose a different number of clusters and start plotting the within-cluster distance to the centroid.

The graph looks as below. Even though the within-cluster distance decreases after 4, we would be doing more computations. Which is just analogous to the law of diminishing returns. Therefore, we choose a value of 4 as the optimum number of clusters.

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The reason it is named the elbow method is that the optimum number of clusters would represent an elbow joint! There are just a few examples where clustering algorithm like K-means is applied. We will be using the Iris dataset to build our algorithm. Even though the Iris dataset has labels, we will be dropping them and use only the feature points to cluster the data.

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We load the dataset and drop the target values. We convert the feature points into a numpy array and split it into training and testing data. We implement the pseudocode shown above and we can find that our algorithm converges after 6 iterations. We can now input a test data point and find the centroid it is closest to and assign that point to the respective cluster.

The Scikit-learn library once again saves us from writing so many lines of code by providing an abstract level object which we can just use to implement the algorithm. K-means is an introductory algorithm to clustering techniques and it is the simplest of them. Hence, no partial derivates is required and that complicated math is eliminated. K-means is an easy to implement and handy algorithm. Sign in. Rohith Gandhi Follow. Introduction — Unsupervised Learning In machine learning, we are not always provided an objective to optimize, we are not always provided a target label to classify the input data points into.

Choose the number of clusters K and obtain the data points 2. Repeat steps 4 and 5 until convergence or until the end of a fixed number of iterations 4.