Clustering With KMeans in Excel | Melakukan pengelompokkan dengan Kmean menggunakan Excel

Summary

This video tutorial explains how to perform clustering using the K-means algorithm in Microsoft Excel. It guides viewers through the process of calculating distances from centroids, assigning data points (students) to clusters, and iterating until the centroids stabilize. The tutorial uses a dataset of student scores (UTS, Tugas, UAS) and demonstrates how to classify students into three clusters: 'kurang' (low), 'sedang' (medium), and 'pintar' (smart). Key Excel functions like SQRT, AVERAGE, and MIN are utilized to simplify the clustering process, with step-by-step instructions and color-coded clusters for easy identification.

Takeaways

• 😀 The tutorial teaches how to apply K-means clustering using Microsoft Excel to group students into three clusters: 'Kurang' (low), 'Sedang' (medium), and 'Pintar' (high).
• 😀 The process starts by selecting initial centroids for the three clusters. These centroids can be chosen randomly or based on the dataset.
• 😀 The Euclidean distance formula is used to calculate the distance from each data point (student) to each centroid.
• 😀 In Excel, the `SQRT` function is used to calculate the square root of the sum of squared differences between the student's grades and the centroid values.
• 😀 After calculating the distances, the student is assigned to the cluster whose centroid is closest to them (minimum distance).
• 😀 Once all students are assigned to clusters, the centroids are recalculated by taking the average of each cluster's grade data (UTS, Tugas, UAS).
• 😀 The process repeats until the cluster assignments stabilize and no longer change between iterations, indicating convergence.
• 😀 Excel's `MIN` function is used to determine which cluster each student belongs to by finding the smallest distance between the student's data and the centroids.
• 😀 The Sum of Squared Errors (SSE) is calculated as a measure of the clustering quality by squaring the distances from each student's data point to their assigned centroid.
• 😀 The final step involves summarizing the students in each cluster: Cluster 1 (Kurang), Cluster 2 (Sedang), and Cluster 3 (Pintar), and assigning each student to their respective cluster based on the closest centroid.
• 😀 The tutorial emphasizes the convenience of using Excel for K-means clustering, making it accessible even for beginners without needing specialized software.

Q & A

What is the purpose of the video?

The purpose of the video is to demonstrate how to use Microsoft Excel to implement a clustering algorithm (likely k-means) for grouping students into three clusters: 'kurang' (low), 'sedang' (medium), and 'pintar' (smart), based on their UTS, Tugas, and UAS scores.

How are the initial centroids for the clusters determined?

The initial centroids are arbitrarily chosen by the user. In this example, the centroids are assigned the values: M1 = (50, 65, 75), M2 = (70, 70, 70), and M3 = (85, 85, 85). These values are used as starting points for clustering the students.

What formula is used to calculate the distance between a student and a centroid?

The Euclidean distance formula is used to calculate the distance between a student’s data point (UTS, Tugas, UAS) and the centroid’s values. The formula is: √((X1 - X_centroid)^2 + (Y1 - Y_centroid)^2 + (Z1 - Z_centroid)^2).

How is the 'MIN' function used in the clustering process?

The 'MIN' function is used to find the smallest distance between a student and the three centroids. After calculating the distances, the student is assigned to the cluster corresponding to the centroid with the smallest distance.

What is the role of the SUM of Squared Errors (SSE) in the clustering process?

The Sum of Squared Errors (SSE) is a measure of the clustering accuracy. It is calculated by summing the squared distances between each student’s data point and its assigned centroid. A lower SSE indicates a better fit for the clusters.

How are the centroids updated after each iteration?

After assigning all students to clusters, the centroids are updated by calculating the average of the UTS, Tugas, and UAS scores for each cluster. These updated centroids are then used in the next iteration.

Why is the process of calculating distances and updating centroids repeated multiple times?

The process is repeated until the centroids no longer change significantly, meaning the algorithm has converged and the clusters are stable. Repeating this process helps refine the clusters and minimize the SSE.

What Excel functions are used to perform the calculations?

The key Excel functions used in this process include 'SQRT' for calculating square roots, 'AVERAGE' for finding the average of cluster members, and 'MIN' for identifying the smallest distance. Additionally, 'F4' is used to lock cells when referencing fixed data.

What happens if the membership of the clusters changes between iterations?

If the membership of the clusters changes between iterations, the centroids are recalculated based on the new cluster assignments. This adjustment continues until the clusters stabilize and no further changes occur.

Can this clustering method be applied to other types of data beyond student scores?

Yes, the k-means clustering method can be applied to any data that has numerical values. For example, it can be used to group customers based on purchasing behavior, segment products based on features, or classify geographical locations based on various attributes.