KDD Process

What does KDD stand for?

• Knowledge Data Discovery

• Knowledge Discovery in Databases

• Knowledge Database Discovery

• Knowledge Discovery in Data

What is the first step in the KDD process?

• Collect data

• Build models

• Evaluate results

• Understand goals and objectives

How many stages in KDD

• 5

• 4

• 6

• 3

Why is data preprocessing important in the KDD process?

• Data preprocessing is only necessary for small datasets

• Data preprocessing is crucial in the KDD process to clean, transform, and prepare raw data for analysis.

• Data preprocessing slows down the KDD process

• Data preprocessing is optional in the KDD process

What are the main steps involved in data preprocessing?

• Data Aggregation

• Data Augmentation

• Data Encryption

• Data Cleaning, Data Transformation, Data Reduction, Data Discretization

How is data mining used in the KDD process?

• Data mining is used to visualize data in the KDD process.

• Data mining is used to create new datasets in the KDD process.

• Data mining is used to extract patterns or knowledge from large datasets in the KDD process.

• Data mining is used to analyze data after the KDD process.

What is the role of pattern evaluation in KDD?

• Pattern evaluation in KDD is primarily focused on data collection.

• Pattern evaluation in KDD is crucial for assessing the relevance, novelty, and value of discovered patterns in solving the data mining task.

• Pattern evaluation in KDD is only useful for visualizing data.

• Pattern evaluation in KDD has no impact on the data mining process.

Discuss the importance of knowledge presentation in the KDD process.

• Knowledge presentation only confuses decision-makers in the KDD process

• Knowledge presentation is irrelevant in the KDD process

• Knowledge presentation slows down the KDD process

• Knowledge presentation plays a vital role in the KDD process by making the insights gained from data mining accessible and interpretable for decision-makers.

What are the challenges faced during the implementation of the KDD process?

• Inadequate domain knowledge

• Limited computing resources

• Lack of data visualization tools

• Data quality, technique selection, large data volumes, privacy/security concerns, result interpretation/validation, knowledge integration

How can the results of the KDD process be validated?

• By consulting a horoscope

• By asking a magic eight ball

• By using techniques such as cross-validation, holdout validation, or using a separate validation dataset.

• By flipping a coin