This qnswer you have to do it on your own no copy from online source Included with this assignment is an Excel spreadsheet that contains data with two dimension values. The purpose of this assignment is to demonstrate steps performed in a K-Means Cluster analysis. Review the “k-MEANS CLUSTERING ALGORITHM” section in Chapter 4 of the Sharda et. al. textbook for additional background. Use Excel to perform the following data analysis. Provide final answers on your Excel spreadsheet indicating your initial center points, second pass center points, and third pass center points.  You must submit your spreadsheet and these answers to receive full credit. You will use Excel to help with calculations, but only standard functions should be used (i.e. don’t use a plug-in to perform the analysis for you.)  You need to show your work doing this analysis the long way.  If you were to repeat steps 4 through 6, what will likely happen with the cluster centroids?  The rubric for this assignment can be viewed when clicking on the assignment link.

K-Means Cluster analysis is a popular technique used in data mining and pattern recognition to partition a set of data points into distinct groups or clusters based on similarity measures. This method aims to minimize the sum of squared distances between data points and their respective cluster centroids.

To perform a K-Means Cluster analysis in Excel, we will need to follow several steps. Firstly, we should determine the number of clusters we want to create, as this is an input parameter for the algorithm. Once we have decided on the number of clusters, we can proceed with the analysis.

The steps involved in a K-Means Cluster analysis are as follows:

1. Select the initial cluster centers: In this step, we randomly select the initial cluster centers. These centers act as representatives for each cluster and will be adjusted iteratively during the algorithm.

2. Assign data points to nearest cluster center: For each data point, calculate the distance to each cluster center and assign the point to the nearest center. This step creates the initial clustering.

3. Calculate new cluster centers: Recalculate the cluster center for each cluster by finding the mean value of all data points belonging to that cluster. This step is crucial as it updates the cluster centers based on the current clustering.

4. Repeat steps 2 and 3: Repeat steps 2 and 3 until the cluster centers no longer change significantly or a maximum number of iterations is reached. This convergence criterion ensures that the algorithm terminates when it finds a stable solution.

Now, let’s consider the assignment requirements. You are provided with an Excel spreadsheet containing data with two dimension values. Using Excel, you need to perform a K-Means Cluster analysis and provide the final answers on your spreadsheet.

To start, you should select the initial cluster centers randomly. Next, calculate the distance between each data point and the cluster centers and assign each point to the nearest center. After that, recompute the cluster centers based on the current clustering. Repeat these steps until the cluster centers no longer change significantly or you reach a predetermined maximum number of iterations.

In your final Excel spreadsheet, you should indicate the initial cluster centers, the cluster centers after the second pass, and the cluster centers after the third pass. This will demonstrate your understanding of the steps performed in the K-Means Cluster analysis and show the evolution of the cluster centers.

If you were to repeat steps 4 through 6, it is likely that the cluster centroids will continue to shift slightly with each iteration. However, as the algorithm converges, the changes in the cluster centroids will become smaller and eventually stabilize, indicating that the clusters have been well-defined.

Remember to follow the instructions and only use standard Excel functions for calculations. Avoid using any external plug-ins or tools that perform the analysis automatically, as the objective of this assignment is to demonstrate your knowledge and understanding of the K-Means Cluster analysis algorithm.

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