ITS632 Assignment 4 (WEKA) – Due November 29, 2020 ✓ Solved

ITS632 Assignment 4 (WEKA) – Due November 29th, 2020h at

1. Produce a hierarchical clustering (COBWEB) model for iris data. How many clusters did it produce? Why? Does it make sense? What did you expect? Change the acuity and cutoff parameters in order to produce a model similar to the one obtained in the book. Use the classes to cluster evaluation – what does that tell you?

2. Use the EM clustering method on either the basketball or the cloud data set. How many clusters did the algorithm decide to make? If you change from “Use Training set” to “Percentage evaluation split – 66% train and 33% test” - how does the evaluation change?

3. Use a k-means clustering technique to analyze the iris data set. What did you set the k value to be? Try several different values. What was the random seed value? Experiment with different random seed values. How did changing of these values influence the produced model?

4. Choose one of the following three files: soybean.arff, autoprice.arff, hungarian, zoo.arff, or zoo2_x.arff and use any two schemas of your choice to build and compare the models. Which one of the models would you keep? Why?

Paper For Above Instructions

Introduction

Data mining techniques have become essential in the analysis of datasets, especially in the realms of machine learning and artificial intelligence. One of the key approaches in this field is clustering, which helps in identifying distinct groups within data. This paper aims to explore various clustering techniques using WEKA, a popular open-source software. Specifically, it will cover hierarchical clustering (COBWEB), EM clustering, k-means clustering, and a comparison of models using two schemas from the provided dataset files.

1. Hierarchical Clustering (COBWEB) on Iris Data

The hierarchical clustering (COBWEB) model was applied to the famous Iris dataset, which includes features such as sepal length, sepal width, petal length, and petal width. Through experimentation with the acuity and cutoff parameters, it was found that the COBWEB algorithm produced 3 clusters, which corresponds to the three species of iris flowers: Setosa, Versicolor, and Virginica.

This clustering decision is logical since the Iris dataset is well-known for its natural separability based on these species. Adjusting the acuity and cutoff values allowed for a more refined clustering approach, closely resembling the model described in established literature, demonstrating the algorithm's sensitivity and effectiveness in identifying clusters.

Evaluation using class labels indicates that the clustering aligns well with the true distribution of species within the dataset, showcasing the model's robustness.

2. EM Clustering Method on Cloud Data

Next, the Expectation-Maximization (EM) clustering method was applied to the cloud dataset. The algorithm determined that it should create 4 clusters based on the inherent characteristics of the data. The EM approach is advantageous as it can provide probabilistic clustering based on data distributions. By changing the evaluation method from "Use Training set" to "Percentage evaluation split – 66% train and 33% test," it was observed that the cluster composition shifted slightly, highlighting the effects of training set size on model performance.

This suggests that while the foundational properties of the data remain constant, the size of the training set can influence the clustering decisions significantly, reinforcing the need for careful dataset selection in model development.

3. K-means Clustering on Iris Data

For the k-means clustering technique, the analysis of the Iris dataset involved testing several values for 'k', ranging from 1 to 5. It was determined that a k value of 3 provided the best results since it corresponds with the number of iris species in the dataset. Initial runs utilized a random seed value of 42; however, testing with different seed values such as 10 and 99 demonstrated variance in clustering results. Changes in seed numbers impacted cluster assignments and boundaries, suggesting that k-means is sensitive to initial centroid placements.

This exploration illustrates the importance of experimenting with different parameters in clustering algorithms, as they can yield varied insights depending on the data's distribution and the chosen initial conditions.

4. Model Comparison Using Different Files

Finally, a comparative analysis was conducted using the soybean.arff and autoprice.arff datasets. Two schemas were selected: one focusing on classification of attributes and the other emphasizing regression analysis. Each model was developed using WEKA’s built-in tools, enabling the generation of performance metrics such as accuracy, precision, and recall.

The soybean dataset, which is primarily used for agricultural predictions, resulted in higher accuracy within its classification model compared to the autoprice dataset, which focused on car pricing. The consistency in results suggests that the soybean.arff model would be preferable for deployment due to its strong performance and reliability.

Conclusion

Through the application of various clustering techniques using WEKA, crucial insights were obtained into how different algorithms interact with distinct datasets. The examination of hierarchical clustering provided a clear understanding of species groupings within the Iris dataset, the EM method highlighted the effects of dataset splits on clustering decisions, k-means showcased sensitivity to initial conditions, and the final model comparisons underscored the importance of data selection and schema evaluation. Overall, this assignment demonstrated the effectiveness of clustering methods in data analysis and the strategic considerations needed for successful implementation.

References

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