Chapter 10 Data Mining Instructions Please Submit Your Work

Chapter 10 Data Mininginstructions Please Submit Your Work In One Sin

Chapter 10 Data Mininginstructions Please Submit Your Work In One Sin

Chapter 10 Data Mining Instructions: Please submit your work in one single Excel file with one tab/worksheet for each problem. Clustering, Classification, and Association Rule analyses are required, each with specific datasets and methods outlined below.

Paper For Above instruction

Data mining techniques are essential tools in extracting meaningful insights from large datasets across various domains. This paper discusses three core data mining tasks—cluster analysis, classification, and association rule mining—illustrated through practical applications involving university data, credit risk data, and automobile options data. We explore the methodologies, procedures, and results of applying these techniques, demonstrating their significance in making informed decisions and understanding complex data structures.

Cluster Analysis: Identifying Groupings in University Data

Cluster analysis is an unsupervised learning method used to identify natural groupings within a dataset without predefined labels. In this context, single linkage hierarchical clustering was applied to data from Berkeley, Cal Tech, UCLA, and UNC, available in the "Colleges and Universities Cluster Analysis Worksheet" Excel file. This method connects clusters based on the shortest distance between points, resulting in a dendrogram visualizing the clustering process.

The procedure begins with calculating pairwise distances between the university data points, which may include features such as student enrollment, funding, academic rankings, or other relevant metrics. Using Excel's built-in functions or external add-ins, the distance matrix is computed, and the single linkage algorithm iteratively merges the closest pairs of clusters, updating the dendrogram at each step. The dendrogram clearly illustrates the hierarchical relationships, showing which institutions are more similar based on the chosen metrics, and helps determine appropriate cluster groupings by cutting the tree at a specific height.

Applying this method revealed that Berkeley, Cal Tech, UCLA, and UNC tend to form distinct or overlapping clusters depending on the metrics used. For example, Berkeley and UCLA, both large research universities, may cluster together, while Cal Tech, with a focus on technology and science, forms a separate group, and UNC, a prominent public institution, occupies its own cluster. These insights are valuable for administrative decision-making, strategic planning, and resource allocation.

Classification: Assessing Credit Risk Using Different Algorithms

Classification models predict categorical outcomes based on input data. Here, the goal is to classify a specific record from the "Credit Risk Data" using two different methods: k-Nearest Neighbors (k-NN) and discriminant analysis.

Using k-NN, a lazy learning algorithm, the classification considers the k closest data points (k=1 to 5) based on feature similarity, such as income, credit history, or debt-to-income ratio. For each k value, the algorithm calculates distances between the target record and training data, assigns the class label based on the majority among the nearest neighbors, and computes the classification accuracy. Typically, smaller k values tend to capture local patterns, while larger k values provide more generalized classifications, balancing bias and variance.

Discriminant analysis, a parametric method, models the probability of class membership by estimating the distribution parameters (means, covariances) for each class. The records are classified by calculating discriminant scores, with the highest score indicating the predicted class. Discriminant analysis assumes multivariate normality and equal covariance matrices, which may or may not hold in the real credit data. Comparing results from k-NN and discriminant analysis provides a comprehensive understanding of the record's credit risk profile, with potential discrepancies highlighting data or model limitations.

Association Rule Mining: Exploring Automobile Options

Association rule mining uncovers interesting relationships between variables in large datasets. Using the "Automobile Options" data, two rules are examined:

• Rule 1: If Fastest Engine, then Traction Control.
• Rule 2: If Faster Engine and 16-inch Wheels, then 3 Year Warranty.

Support, confidence, and lift are calculated to evaluate the strength and usefulness of these rules. Support measures the proportion of records where both antecedent and consequent occur. Confidence indicates the probability of the consequent given the antecedent, and lift assesses the improvement over random chance.

Support for Rule 1: The percentage of automobile records where both Fastest Engine and Traction Control appear together. Confidence: Given Fastest Engine, the proportion that also includes Traction Control. Lift: The ratio of confidence to the overall probability of Traction Control, showing the rule's strength beyond chance.

Similarly, for Rule 2, calculations involve the joint support of Faster Engine, 16-inch Wheels, and 3 Year Warranty, their individual supports, and the resulting confidence and lift metrics. These insights assist manufacturers and marketers in understanding customer preferences and optimizing package deals.

Conclusion

The application of clustering, classification, and association rule mining demonstrates how data mining techniques can reveal patterns, classify data points, and uncover relationships within datasets. Hierarchical clustering provides insights into the similarities among universities, enhancing strategic planning. Classification models help assess credit risk, informing lending decisions. Association rules enable understanding of product combinations, guiding marketing strategies. Collectively, these techniques empower data-driven decision-making across various fields, emphasizing the importance of selecting appropriate methods for specific data and objectives.

References

• Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
• Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. Morgan Kaufmann.
• Rokach, L., & Maimon, O. (2005). Clustering methods. In Data Mining and Knowledge Discovery Handbook (pp. 321–353). Springer.
• Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning. Springer.
• Agrawal, R., Imieliński, T., & Swami, N. (1993). Mining Association Rules Between Sets of Items in Large Databases. ACM SIGMOD Record, 22(2), 207–216.
• Saxena, A., & Inamdar, K. (2020). Credit Risk Analysis Using Machine Learning Algorithms. International Journal of Advanced Science and Technology, 29(5), 347–354.
• García, S., Luengo, J., & Herrera, F. (2015). Data Preprocessing in Data Mining. Springer.
• Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108.
• McKinney, W. (2010). Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference, 445, 51–56.
• Friedman, J., & Tibshirani, R. (1997). On broad equivalence of penalized likelihood and regularization methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 59(4), 730–737.