Data Mining Preprocessing

What is data cleaning and why is it important in data mining preprocessing?

• Data cleaning is not necessary in data mining preprocessing.

• Data cleaning involves adding more errors to the dataset.

• Data cleaning is the process of removing all data from the dataset.

• Data cleaning is the process of identifying and correcting errors or inconsistencies in data to improve its quality. It is important to have accurate and reliable results during analysis.

Explain the difference between data transformation and data reduction in the context of data mining preprocessing.

• Data reduction involves converting data into a suitable format for mining.

• Data addition involves increasing the volume of data for better analysis.

• Data transformation is converting data into a suitable format for such as normalization or aggregation. Data reduction aims to reduce the volume using feature selection or dimensionality reduction.

• Data transformation aims to reduce the volume of data for faster processing.

How can missing values in a dataset be handled effectively during data preprocessing?

• By removing rows with missing values, imputing missing values with mean, median, or mode, or using advanced techniques like predictive modeling.

• By replacing missing values with random numbers

• By doubling the existing values

• By ignoring missing values completely

Data integration.

• Data integration is not necessary for data mining

• Data integration does not impact the quality of data mining results

• Data integration involves only one source of data

• Data integration involves gathering data from various sources, transforming it into a consistent format, and loading it into a target database or data warehouse. This unified data set provides a more complete picture for data mining algorithms to analyze and extract valuable patterns and insights.

What are the different approaches to data discretization ?

• Logistic Regression

• K-Means Clustering

• Equal Width Binning, Equal Frequency Binning, Clustering

• Random Forest

Name and explain three common data cleaning techniques used in data preprocessing.

• Feature engineering

• Outlier detection

• Data shuffling

• Removing duplicates, Handling missing values, Standardizing data

Describe the steps involved in data transformation methods during data mining preprocessing.

• Standardization

• Dimensionality reduction

• Outlier detection

• Normalization, Encoding categorical variables, Handling missing values, Feature scaling

What are the main strategies for data reduction and how do they help in preprocessing?

• Data duplication, data amplification, data expansion

• Feature selection, feature extraction, and dimensionality reduction

Why is it crucial to handle missing values appropriately in a dataset before data mining?

• Missing values have no impact on data mining

• Missing values improve model accuracy

• Missing values can lead to biased results, inaccurate predictions, and reduced model performance.

• Handling missing values is time-consuming

How does data integration contribute to the overall success of data mining projects?

• Data integration contributes to the overall success of data mining projects by improving data quality, completeness, and enabling a more comprehensive analysis.

• Data integration has no impact on the success of data mining projects

• Data integration slows down the analysis process in data mining projects

• Data integration hinders the data quality in data mining projects