K means Clustering Algorithm in ML - Types and Applications

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The K-means clustering algorithm is a simple way to group data. It's like putting similar things together, such as sorting toys into boxes based on color. K-means does something identical with numbers and data points. We'll learn how it works and try it out with Python. Along with that, we will clear all your doubts regarding this algorithm in machine learning. 

Overview of K means Clustering Algorithm in Machine Learning

K-means clustering is a simple yet effective technique for grouping similar data points. It's like sorting toys into boxes based on color. K-means finds groups of data points that are close together. You choose several groups (K), and K-means puts each data point in the closest group. As a result, this helps you find patterns and structures within your data. 

Clustering K means in machine learning is a simple way to group data. In addition, it works by putting data points into groups based on how close they are to a group's center. First, it chooses random centers for the groups. Then, it puts each data point in the closest group. After that, it finds new centers for the groups. However, this process repeats until it finds the best groups. You need to know how many groups you want before you start.

K-means clustering algorithm is a simple way to group data. However, it has some limitations. Sometimes, it's hard to decide how many groups (K) to use. It works best when the data is separated, but it struggles when data points overlap. K-means is fast, but it might not find the best groups. It also doesn't tell you how good the groups are. If you start with different groups, you might get different results. K-means can also be affected by noise in the data. It might get stuck in a bad spot.

Types of Clustering K Means in Machine Learning

Clustering is a way to group similar things. There are two primary ways to do this:

• Hierarchical clustering: It starts with everyone in one big group and then splits them into smaller groups until everyone is in their group.
• Partitioning clustering: This is like dividing a class into teams. You start with several teams and then move people around until everyone is on the right team.

Hierarchical clustering can be done in two ways:

• Agglomerative clustering: This starts with everyone in their group and then combines similar groups.
• Divisive clustering: This starts with everyone in one big group and then splits the group into smaller groups.

Partitioning clustering can also be done in two ways:

• K-means clustering: This is a popular way to group data. It chooses several groups (K) and then puts each data point in the closest group.
• Fuzzy C-means clustering: This is similar to K-means, but it allows data points to belong to more than one group at a time.

The Objective of K Means Clustering

Clustering is a technique used to group similar data points. For example, it’s sorting toys into boxes based on color. By grouping data points with common characteristics, clustering helps you identify patterns, trends, and relationships within your data. As a result, this can be useful for tasks like customer segmentation, image analysis, and anomaly detection.

How K Means Clustering Works?

K-means clustering algorithm is an unsupervised learning algorithm that divides a dataset into a pre-defined number of clusters. The goal is to group similar data points and discover underlying patterns or structures within the data.   

The algorithm works by following these steps:

• Initialization: Randomly select K data points as initial centroids, which represent the centers of the clusters.
• Assignment: Assign each data point to the nearest centroid based on Euclidean distance. This creates initial clusters.
• Update: Calculate new centroids for each cluster by taking the average of all data points assigned to that cluster.
• Reprise: Repeat steps 2 and 3 until the centroids no longer change significantly or a maximum number of iterations is reached.

The final clusters represent groups of similar data points, and the centroids serve as representative points for each cluster.   

Key points to remember:

• The choice of K, the number of clusters, is crucial and can significantly impact the results.
• K-means is sensitive to the initialization of centroids. Different initializations can lead to different clustering results.
• K-means assumes that clusters are spherical and of equal size, which may not always be the case in real-world data.
• For large datasets, the K-means clustering algorithm can be computationally expensive, especially for high-dimensional data.

Despite these limitations, K-means is a popular and widely used clustering algorithm due to its simplicity and efficiency. Moreover, it is often used in various applications, including customer segmentation, image segmentation, and anomaly detection.

Applications of K Means Clustering

K-means clustering algorithm is a simple way to group data. It's like putting similar things together. It has many uses, like:

• Customer groups: Banks can group customers based on how they use their accounts. In addition, this helps them offer personalized deals. 
• Image parts: K-means can find similar parts of an image, which is useful for identifying things in pictures.
• Song suggestions: K-means can suggest songs based on what you like to listen to.
• Traffic patterns: K-means can find patterns in traffic data to understand where traffic is slow.
• Image compression: K-means can make images smaller without losing much quality.
• Finding fraud: K-means can help detect fake activity.
• Predicting customer loss: K-means can help predict when customers might stop using a service.
• Finding cybercrime: K-means can help find signs of cybercrime.

Example of K Means Clustering 

The following section will elaborate on various examples. Read and go through them. 

1. sns.set_style("whitegrid")

g=sns.lineplot(x=range(1,11), y=sse)

g.set(xlabel ="Number of cluster (k)", 

      ylabel = "Sum Squared Error", 

      title ='Elbow Method')

plt.show()
 

2.  plt.scatter(X[:,0],X[:,1],c = pred)

for i in clusters:

    center = clusters[i]['center']

    plt.scatter(center[0],center[1],marker = '^',c = 'red')

plt.show()
 

3. def pred_cluster(X, clusters):

    pred = []

    for i in range(X.shape[0]):

        dist = []

        for j in range(k):

            dist.append(distance(X[i],clusters[j]['center']))

        pred.append(np.argmin(dist))

    return pred   

Conclusion

K-means clustering algorithm is a simple yet effective technique for grouping similar data points. Moreover, it's a popular unsupervised learning algorithm that has a wide range of applications, from customer segmentation to image analysis. While K-means is easy to understand and implement, it's important to know its limitations, such as sensitivity to initialization and the assumption of spherical clusters. By understanding these limitations and using K-means appropriately, you can effectively leverage its power to uncover valuable insights from your data.  

Frequently Asked Questions