What is Data Mining and knowledge discovery? Explain how the evolution of database technology led to data mining? Discuss major stages in data mining regarding mining methodology,... What is Data Mining and knowledge discovery? Explain how the evolution of database technology led to data mining? Discuss major stages in data mining regarding mining methodology, user interaction, performance and diverse data types. Explain constraint based association mining. What is Apriori algorithm? Give few techniques to improve the efficiency of apriori algorithm. Describe the partitioning and density based methods of clustering. Write about data mining currently available tools. Read more

Data mining extracts valuable patterns from large datasets using KDD, evolving from database technology advancements. Key stages are methodology, user interaction, performance, and handling diverse data types. Apriori algorithm finds frequent itemsets; partitioning and density-based methods are key clustering techniques. Tools include KNIME and RapidMiner.

Data mining is the process of extracting useful patterns and knowledge from large datasets. Knowledge Discovery in Databases (KDD) involves several steps, including data cleaning, integration, selection, transformation, data mining, pattern evaluation, and knowledge presentation. The evolution of database technology, from simple file systems to complex data warehousing, enabled data mining by increasing data storage and management capabilities.

Major stages in data mining include:

• Mining Methodology: Techniques like clustering, classification, association, and regression are used.
• User Interaction: Involves tools that allow users to explore data visually and interactively.
• Performance: Efficiency and scalability of algorithms are crucial.
• Diverse Data Types: Handling structured, unstructured, multimedia, and spatial data.

Constraint-based association mining focuses on finding interesting patterns by applying constraints such as frequency, monetary value, or other thresholds.

The Apriori algorithm identifies frequent itemsets in transactional databases and derives association rules. To improve its efficiency, techniques such as reducing the number of database scans, hashing, and partitioning can be used.

Clustering methods include:

• Partitioning: Divides data into distinct groups based on proximity.
• Density-Based: Identifies clusters of varying densities, identifying noise and outliers.

Current data mining tools include KNIME, RapidMiner, and Weka, which offer various functionalities for data analysis and machine learning.