Data Mining Techniques and Applications

The video provides an overview of data mining, explaining various techniques such as classification, clustering, regression, association rule mining, text mining, time series analysis, decision trees, neural networks, collaborative filtering, and dimensionality reduction. Each technique is described with its applications and benefits, helping businesses gain insights and make informed decisions.

What is the primary goal of data mining?

• To store large datasets efficiently

• To delete irrelevant data

• To extract useful insights from data

• To create new data from existing data

Which of the following is a common application of classification?

• Spam filtering

• Image compression

• Data encryption

• Weather forecasting

What is the main objective of clustering in data mining?

• To predict future trends

• To group similar data points

• To classify data into predefined categories

• To reduce data dimensions

In regression analysis, what is the dependent variable also known as?

• Feature variable

• Independent variable

• Response variable

• Predictor variable

Which technique is typically used in market basket analysis?

• Time series analysis

• Text mining

• Association rule mining

• Clustering

What is the main goal of text mining?

• To convert structured data into unstructured data

• To visualize data trends

• To analyze numerical data

• To extract useful information from unstructured text

Which of the following is an advantage of time series analysis?

• It ignores seasonality in data

• It only works with univariate data

• It provides forecasts and confidence intervals

• It cannot handle missing data

What is a key advantage of decision trees?

• They require a lot of data preprocessing

• They are simple and interpretable

• They can only handle categorical data

• They are complex and hard to interpret

What is the primary function of neural networks?

• To mimic human brain processing

• To store large amounts of data

• To simplify data structures

• To reduce data dimensions

What is the purpose of dimensionality reduction?

• To increase the number of features in a dataset

• To reduce the number of features while retaining information

• To eliminate all irrelevant data

• To convert data into a higher-dimensional space