DON’T BID IF YOU CANT DELIVER IN 4HRS….Please implement K-…

DON’T BID IF YOU CANT DELIVER IN 4HRS…. Please implement K-means clustering, according to the algorithm introduced in the class. (You may want to refer to the slides) and run it on IRIS data, which can be downloaded from use only one attribute/feature (column) of the data

Answer

K-means clustering is a widely used unsupervised machine learning algorithm that aims to partition a dataset into k distinct clusters. The algorithm, introduced by Stuart Lloyd in 1957, has since found numerous applications in various fields, including data mining, image processing, and pattern recognition. In this assignment, we will implement the K-means clustering algorithm on the Iris dataset, utilizing only one attribute/feature (column) of the data.

The Iris dataset is a commonly used dataset in machine learning. It consists of measurements of four features (sepal length, sepal width, petal length, and petal width) for three different species of Iris flowers (setosa, versicolor, and virginica). For our analysis, we will focus on a single feature and disregard the other three.

To implement the K-means clustering algorithm, we will follow the basic steps outlined in the class slides. These steps can be summarized as follows:

Step 1: Choose the number of clusters, k, that we want to create. In this assignment, since we are utilizing only one attribute/feature, our clusters will represent distinct ranges of values for that feature.

Step 2: Initialize the centroids by randomly assigning k points as the initial cluster centers.

Step 3: Assign each data point to the nearest centroid. This can be done by computing the Euclidean distance between each data point and the centroids and assigning the data point to the cluster with the nearest centroid.

Step 4: Recalculate the centroids by computing the mean of all the data points assigned to each cluster. The mean value will serve as the new centroid.

Step 5: Repeat steps 3 and 4 until convergence is achieved. Convergence occurs when the centroids no longer change significantly or when a predetermined number of iterations is reached.

Once the K-means algorithm has been implemented, we will apply it to the Iris dataset using only one feature. The specific feature we choose will depend on the requirements of the assignment and the research question we seek to answer. For example, we may choose the sepal length as our attribute of interest and aim to identify distinct clusters based on different ranges of sepal length.

After running the K-means clustering algorithm on the Iris dataset, we can evaluate the results by examining the cluster assignments and the centroids. Visualizations, such as scatter plots or histograms, can provide a helpful representation of the clusters and their relationship to the chosen attribute.

In conclusion, this assignment will involve implementing the K-means clustering algorithm on the Iris dataset, using only one attribute/feature. By following the steps outlined in the class slides, we can partition the data into distinct clusters based on the chosen feature and evaluate the results for further analysis and interpretation.