KDD vs Data Mining

 KDD vs Data Mining

 KDD (Knowledge Discovery in Databases) is a process in computer science that helps find useful and previously hidden information (knowledge) from large amounts of data. It involves several steps, and one of these steps is Data Mining. Data Mining uses specific methods or algorithms to find patterns in the data. Although KDD includes multiple steps, people often use "KDD" and "Data Mining" to mean the same thing.

What is KDD?

KDD (Knowledge Discovery in Databases) is a field in computer science focused on finding useful and previously hidden information in raw data. It involves creating methods to analyze and summarize data, making it easier to understand and use. This process turns raw data into more compact and meaningful forms, such as reports, patterns, or models that can predict future events.

As data has grown rapidly, especially in business, KDD has become essential for turning large amounts of data into valuable insights. Manual pattern analysis is no longer practical, so KDD helps automate the process.

For example, KDD is used in areas like social network analysis, fraud detection, science, investing, manufacturing, telecommunications, sports, and marketing. It helps answer questions like, "What products could bring the most profit next year for V-Mart?"

KDD Process Steps

Knowledge discovery in the database process includes the following steps, such as:

1. Goal Identification: Understand the problem, the application area, and the customer’s needs to define clear goals for the KDD process.

2. Selecting Target Data: Choose the specific data or variables to focus on for analysis.

3. Data Cleaning and Preprocessing: Prepare the data by removing errors, handling missing values, and organizing it for analysis.

4. Data Reduction and Transformation: Simplify the data by reducing the number of variables or finding new ways to represent it, like summarizing or transforming it.

5. Aligning with Goals: Match the mining process to the goals, such as identifying patterns, classifying data, or grouping similar items.

6. Modeling and Method Selection: Pick the right algorithms and techniques to analyze the data, based on the problem and the type of data.

7. Data Mining: Extract useful patterns or insights, such as classification rules, clusters, or trends.

8. Evaluation and Presentation: Review and interpret the findings, refine the process if needed, and present the results in an understandable way, like charts or reports.

9. Taking Action: Use the discovered knowledge to make decisions, improve processes, or inform stakeholders. This may involve comparing the new insights with what was already known.

Data mining

Data mining, also called Knowledge Discovery in Databases, is the process of finding hidden, useful, and meaningful information in large sets of data stored in databases.

Key Points:

• Part of KDD: Data mining is just one step in the overall KDD process.
• Two Main Goals:
  1. Verification: Tests a hypothesis or idea the user has about the data.
  2. Discovery: Automatically uncovers interesting patterns without prior assumptions.

Major Data Mining Tasks:

1. Clustering: Grouping similar items from unstructured data.
2. Classification: Creating rules to categorize new data.
3. Regression: Finding relationships or functions to predict values with minimal error.
4. Association: Identifying relationships between variables.

Different algorithms like linear regression, decision trees, or Naive Bayes are used based on the goal. After finding patterns, the results are evaluated for accuracy or ease of understanding.

 Need Data Mining

The amount of data from sources like business, science, sensors, pictures, and videos grows every day. Data mining helps by summarizing this data, identifying patterns, and creating reports or insights automatically. This makes decision-making faster and more effective.

Data Mining used in business 

Data mining helps businesses make better decisions by:

1.Automatically Summarizing  Data

2.Finding Patterns

3.Extracting Useful Information

KDD and Data Mining

In today’s world, data is everywhere, but it’s only useful if we can organize and analyze it to find its true value.

Many industries gather huge amounts of data, but raw data alone isn’t helpful unless it’s filtered and visualized through graphs, charts, or trends.

The challenge is that the amount of data and how quickly it’s collected make it hard to manage. This has made it essential to improve our tools and methods for analyzing large datasets.

Since computers let us collect more data than we can handle manually, we rely on computational techniques to identify meaningful patterns and insights from this vast data.

Difference between KDD and Data Mining

KDD (Knowledge Discovery in Databases) and Data Mining are closely related but not the same.

• KDD is the entire process of finding useful knowledge in data.
• Data Mining is just one step in KDD, focused on finding patterns in the data using specific algorithms.

KDD is an ongoing process where results are evaluated, the methods can be improved, and new data can be added or transformed to get better and more accurate insights.