DATA MINING seminar prjzkpwnshzghBwkwodoxjz

Presentation

1. TOPIC -DATA MINING NAME-HITESH KUMAR PADHIARY REGD NO-2101104076 BRANCH-COMPUTER SCIENCE AND ENGINNERING SEMESTER- 5TH GUIDED BY-MRS. SASMITA PANI

2. What is Data Mining? Data mining is the process of extracting useful patterns and insights from large datasets. It involves analyzing data to discover hidden relationships, trends, and patterns that can be used to make informed decisions.

3. History of Data Mining 1 1960s-1970s: Early Development Data mining techniques began to emerge as researchers explored different approaches to analyze data and discover patterns. 2 1980s-1990s: Advancements in Algorithms New algorithms and methodologies were developed, enabling more efficient and effective data mining processes. 3 2000s-Present: Big Data Era The explosion of digital data and the advent of technologies like Hadoop and cloud computing revolutionized data mining, allowing for the analysis of massive datasets.

4. Processes for Data Mining 1 Association Rule Mining Discover relationships between items frequently purchased together to improve cross-selling strategies. 2 Classification Categorize data into predefined classes based on their attributes and characteristics. 3 Clustering Group similar data points based on their similarity or proximity to each other. 4 Regression Predict numeric values or continuous outcomes based on historical data.

5. Classification 1 Decision Trees Build a tree-like model to classify data based on a set of rules. 2 Support Vector Machines Create a hyperplane to separate data into different classes. 3 Random Forest Combine multiple decision trees to improve classification accuracy and handle larger datasets.

6. Applications of Data Mining Business Identify customer buying behavior, fraud detection, and market segmentation. Healthcare Predict disease outcomes, patient monitoring, and medical diagnosis. E-Commerce Personalize recommendations, customer segmentation, and churn prediction.

7. Challenges and Ethical Issues in Data Mining 1 Data Privacy Protecting sensitive information and ensuring compliance with privacy regulations. 2 Bias and Fairness Avoiding discriminatory outcomes and addressing biases in training data. 3 Data Quality Handling noisy and incomplete data to maintain accuracy and reliability.

8. Conclusion Based on the information presented, it is clear that data mining is a powerful tool for uncovering valuable insights from large datasets. By applying various techniques such as association rule mining, classification, clustering, and regression, businesses and organizations can make informed decisions and improve their processes. As technology continues to advance and the amount of available data grows, data mining will play an increasingly important role in driving innovation and success.

9. REFERENCE WWW.GOOGLE.COM WWW.WIKIPEDIA.COM

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