{"id":8091,"date":"2021-01-11T18:49:59","date_gmt":"2021-01-11T18:49:59","guid":{"rendered":"https:\/\/wealthrevelation.com\/data-science\/2021\/01\/11\/five-most-popular-unsupervised-learning-algorithms\/"},"modified":"2021-01-11T18:49:59","modified_gmt":"2021-01-11T18:49:59","slug":"five-most-popular-unsupervised-learning-algorithms","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2021\/01\/11\/five-most-popular-unsupervised-learning-algorithms\/","title":{"rendered":"Five Most Popular Unsupervised Learning Algorithms"},"content":{"rendered":"
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\"Unsupervised\"Unsupervised<\/span><\/div>\n
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\u00a0 \u00a0Today we are going to learn about the popular unsupervised learning algorithms in machine learning<\/strong><\/a>. Before that let\u2019s talk about a fun puzzle.<\/p>\n

Have you ever done a complete-the-pattern puzzle?\u00a0<\/p><\/blockquote>\n

Where, we do some shapes of different designs presented in a row, and you have to suppose what the next form is going to be.<\/p>\n

It is interesting, right?\u00a0<\/p>\n

Although we have never seen those sorts of puzzles before, we are still able to figure it rightly (Haha, not every time)<\/p>\n

So, what we are doing here is pattern recognition. It depends on what we see and guess a trend or pattern in the given data. <\/p>\n

We analyze the whole data. Draw some conclusions, and, based on that, predict the next occurring shape or design.<\/p>\n<\/div>\n

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Learn the most popular unsupervised learning algorithms in machine learning #machinelearning #datascience #python #clustering\u00a0<\/p>\n<\/p><\/div>\n

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Well, unsupervised learning algorithms also follow the same approach for solving the real-world problems.\u00a0<\/p>\n

In this article, we are going to discuss different unsupervised machine learning algorithms. We will also cover the proper functioning of these unsupervised machine learning algorithms.<\/p>\n

This unsupervised machine learning algorithms article help you like a quick recap for brush up the topics you can refer while you are preparing for the data science jobs<\/strong><\/a>.<\/p>\n

Before we begin, let\u2019s look at the topics you are going to learn.<\/p>\n<\/div>\n

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Let\u2019s start the article by discussing unsupervised learning.<\/p>\n

What is Unsupervised Machine learning?<\/h2>\n<\/div>\n
\"Unsupervised\"Unsupervised<\/span><\/div>\n
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Unsupervised learning is a machine learning approach in which models do not<\/strong> have any supervisor to guide them. Models themselves find the hidden patterns and insights from the provided data.\u00a0<\/p>\n

It mainly handles the unlabelled data. Somebody can compare it to learning, which occurs when a student solves problems without a teacher\u2019s supervision.\u00a0<\/p>\n

We cannot apply unsupervised learning directly to a regression or classification<\/strong><\/a> problem. Because like supervised learning<\/strong><\/a>, we don\u2019t have the input data with the corresponding output label.\u00a0<\/p>\n

Unsupervised learning aims to discover the dataset\u2019s underlying pattern<\/strong>, assemble that data according to similarities<\/strong><\/a>, and express that dataset in a precise format.<\/p>\n

Unsupervised Learning Algorithms allow users to perform more advanced processing jobs compared to supervised learning. <\/p>\n

However, unsupervised learning can be more irregular compared with other methods.\u00a0<\/p>\n

Example: \u00a0<\/strong><\/p>\n

Assume we have x input<\/strong> variables, then there would be no corresponding output variable. The algorithms need to find an informative pattern in the given data for learning.<\/p>\n

Why use an Unsupervised Learning algorithm?<\/h2>\n

There are various reasons which illustrate the importance of Unsupervised Learning:<\/p>\n

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  1. \n

    It is similar to how a\u00a0 human learns. It involves thinking by experiences, which moves it closer to real AI.<\/p>\n<\/li>\n

  2. \n

    It works on unlabeled data, which makes unsupervised learning further critical as real-world data is mostly unlabelled.\u00a0<\/p>\n<\/li>\n

  3. \n

    It helps look for useful insights from the data.<\/p>\n<\/li>\n<\/ol>\n

    By now, we have covered all the basics of unsupervised learning. Now, let us discuss different unsupervised machine learning algorithms.\u00a0<\/p>\n

    Types of Unsupervised Learning Algorithms<\/h2>\n<\/div>\n
    \"Unsupervised\"Unsupervised<\/span><\/div>\n
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    There are the following types of unsupervised\u00a0 machine learning algorithms:\u00a0<\/p>\n

    Let us analyze them in more depth.<\/p>\n

    K-means Clustering<\/h2>\n<\/div>\n
    \"K-means\"K-means<\/span><\/div>\n
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    K-Means Clustering is an Unsupervised Learning algorithm. It arranges the unlabeled dataset into several clusters.\u00a0<\/p>\n

    Here K denotes<\/strong> the number of pre-defined groups. K can hold any random value, as if K=3, there will be three clusters, and for K=4, there will be four clusters.\u00a0<\/p>\n

    It is a repetitive algorithm that splits the given unlabeled dataset into K clusters.\u00a0<\/p>\n

    Each dataset belongs to only one group that has related properties. It enables us to collect the data into several groups.\u00a0<\/p>\n

    It is a handy method to identify the categories of groups in the given dataset without training.<\/p>\n

    How does the K-means algorithm work<\/h3>\n

    The functioning of the K-Means algorithm describes as following :<\/p>\n

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    1. Choose the number K to determine the number of clusters.<\/li>\n
    2. Select arbitrary K points or centroids. (It can be different from the input dataset).<\/li>\n
    3. Assign all data points to their nearest centroid. It will create the predetermined \u00a0K clusters.<\/li>\n
    4. Calculate the variance and put a new centroid of each cluster.<\/li>\n
    5. Repeat the third step. Keep reassigning each data point to the latest cluster\u2019s closest centroid.<\/li>\n
    6. If any reassignment happens, then move to step-4; else, end.<\/li>\n
    7. Finally, your model is ready.<\/li>\n<\/ol>\n

      There are several difficulties with K-means. It regularly seeks to make clusters of a similar size.\u00a0<\/p>\n

      Additionally, we have to determine the number of groups at the starting of the algorithm. We do not know how many clusters we have to choose from at the starting of the algorithm. It\u2019s a challenge with K-means.<\/p>\n

      If you would like to learn more about the k-means clustering algorithm please check the below article.<\/p>\n

      Hierarchical clustering<\/h2>\n<\/div>\n
      \"Hierarchical\"Hierarchical<\/span><\/div>\n
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      Hierarchical clustering, also known as Hierarchical cluster analysis. It is an unsupervised clustering algorithm<\/strong><\/a>. It includes building clusters that have a preliminary order from top to bottom.<\/p>\n

      For example, All files and folders on the hard disk are in a hierarchy.<\/p>\n

      The algorithm clubs related objects into groups named clusters. Finally, we get a set of clusters or groups. Here each cluster is different from the other cluster.\u00a0<\/p>\n

      Also, the data points in each cluster are broadly related to each other.<\/p>\n<\/div>\n

      \"Hierarchical\"Hierarchical<\/span><\/div>\n
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      Two types of Hierarchical clustering method are:<\/p>\n

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      1. Agglomerative Hierarchical Clustering<\/li>\n
      2. Divisive Hierarchical Clustering<\/li>\n<\/ol>\n

        Agglomerative Hierarchical Clustering<\/h3>\n

        \u00a0In an agglomerative hierarchical algorithm, each data point is considered a single cluster. Then these clusters successively unite or agglomerate (bottom-up approach) the clusters\u2019 sets. The hierarchy of the clusters is shown using a dendrogram.<\/p>\n

        Divisive Hierarchical Clustering<\/h3>\n

        In a divisive hierarchical algorithm, all the data points form one colossal cluster. The clustering method involves partitioning (Top-down approach) one massive cluster into several small clusters.<\/p>\n

        How does Agglomerative Hierarchical Clustering Works<\/h3>\n

        The functioning of the K-Means algorithm is :<\/p>\n