CMT/CMM426: Data Mining Concepts

What is clustering in data mining?

• Clustering is the process of sorting data alphabetically.

• Clustering is the process of grouping similar data points together.

• Clustering is the process of predicting future trends based on past data.

• Clustering is the process of removing outliers from a dataset.

Give an example of a clustering algorithm.

• Linear regression

• K-means clustering

• DBSCAN clustering

• Hierarchical clustering

Explain the difference between supervised and unsupervised classification.

• The main difference is the use of labeled data in supervised classification and the lack of labeled data in unsupervised classification.

• Supervised classification is used for clustering, while unsupervised classification is used for regression.

• Supervised classification requires human intervention, while unsupervised classification is fully automated.

• Supervised classification uses images, while unsupervised classification uses text data.

What is the purpose of association rules in data mining?

• Association rules are used to discover relationships between variables in large datasets.

• Association rules are used to predict future outcomes based on historical data.

• Association rules are used to visualize data patterns in a graphical format.

• Association rules are used to clean and preprocess raw data before analysis.

How is support calculated in association rule mining?

• Support is calculated by dividing the number of transactions containing the itemset by the total number of transactions.

• Support is calculated by multiplying the number of transactions containing the itemset by the total number of transactions.

• Support is calculated by taking the square root of the number of transactions containing the itemset.

• Support is calculated by subtracting the number of transactions containing the itemset from the total number of transactions.

What is the main goal of classification in data mining?

• Maximize the accuracy of the model

• Identify outliers in the dataset

• Minimize the number of features used for prediction

• Accurately predict the target class for each data instance based on the input attributes.

What are the key components of association rule mining?

• support, conviction, and confidence

• confidence, lift, and conviction

• confidence, conviction, and leverage

• support, confidence, and lift

How does the K-means algorithm work in clustering?

• Assign data points to clusters based on farthest centroid.

• Update centroids without reassigning data points to clusters.

• Assign data points randomly to clusters without considering centroids.

• Assign data points to clusters based on nearest centroid and update centroids iteratively.

What is the Apriori algorithm used for in association rule mining?

• To calculate shortest path algorithms

• To analyze sentiment in text data

• To predict stock market trends

• To find frequent itemsets