Exploring Data Representation and Outliers

What are the main types of data representation in data mining?

• Graph data

• Structured data, unstructured data, semi-structured data, time-series data.

• Spatial data

• Categorical data

How does structured data differ from unstructured data?

• Structured data is organized and easily searchable, while unstructured data is unorganized and harder to analyze.

• Structured data is always in text format, while unstructured data is in numerical format.

• Structured data can only be stored in databases, whereas unstructured data can only be stored in files.

• Unstructured data is organized and easily searchable, while structured data is unorganized and harder to analyze.

What is the purpose of data normalization in data representation?

• To increase data redundancy and decrease data integrity.

• To enhance data visualization and presentation.

• To simplify data storage without affecting data quality.

• The purpose of data normalization is to reduce data redundancy and improve data integrity.

Name two common methods for outlier detection.

• Median method

• Z-score method, IQR method

• Mean method

• Standard deviation method

What is the Z-score method used for in outlier analysis?

• To visualize data distribution using histograms.

• To identify outliers by measuring how far data points deviate from the mean.

• To calculate the mean of the dataset.

• To determine the median of the data points.

What role does clustering play in outlier detection?

• Clustering is used to increase the number of data points in a dataset.

• Clustering has no impact on the identification of outliers.

• Clustering eliminates outliers by merging them with nearby points.

• Clustering helps identify outliers by grouping similar data points and highlighting those that do not fit well within any cluster.

How can statistical tests be used to identify outliers?

• Statistical tests can only confirm data accuracy.

• Outliers are always removed from datasets automatically.

• Statistical tests are not applicable to small sample sizes.

• Statistical tests like z-scores and IQR can identify outliers.

List three data visualization techniques used to identify outliers.

• Area charts

• Pie charts

• Box plots, scatter plots, histograms

• Line graphs

How can box plots be useful in visualizing outliers?

• Box plots are primarily used for displaying trends over time.

• Box plots do not show any data points.

• Box plots only display the mean of the data.

• Box plots are useful in visualizing outliers by clearly marking them as points outside the whiskers.

What is the purpose of scatter plots in outlier detection?

• Scatter plots are used to calculate the mean of data points.

• Scatter plots are primarily for visualizing time series data.

• Scatter plots help in identifying outliers by showing data points that deviate significantly from the overall distribution.

• Scatter plots display only the average values of datasets.