What are the seminal papers on data mining?

Data mining is a multidisciplinary field that incorporates techniques from statistics, machine learning, and database systems to discover patterns and knowledge from large datasets. Several seminal papers have contributed to the foundation and development of data mining. Here are some key papers that have had a significant impact on the field:

1. "A Few Useful Things to Know About Machine Learning" by Pedro Domingos (1997):

   • This paper provides practical advice and insights into various aspects of machine learning, including data mining. It covers issues such as overfitting, the curse of dimensionality, and the importance of feature engineering.

2. "Data Mining: Concepts and Techniques" by Jiawei Han and Micheline Kamber (2000):

   • While not a traditional research paper, this book has been highly influential in the field of data mining. It provides a comprehensive overview of the key concepts, techniques, and applications of data mining.

3. "Mining Association Rules Between Sets of Items in Large Databases" by Rakesh Agrawal, Tomasz Imieliński, and Arun Swami (1993):

   • This paper introduces the concept of association rules, which is a fundamental technique in data mining. Association rule mining is used to discover interesting relationships and patterns within large datasets.

4. "Fast Algorithms for Mining Association Rules" by Rakesh Agrawal and Ramakrishnan Srikant (1994):

   • Building upon their previous work, the authors propose efficient algorithms for mining association rules. This paper addresses the scalability issues associated with mining large datasets.

5. "Knowledge Discovery in Databases: An Overview" by Gregory Piatetsky-Shapiro and William Frawley (1991):

   • This paper provides an early overview of the knowledge discovery process and the role of data mining within it. It discusses the challenges and opportunities in extracting useful knowledge from large datasets.

6. "Support-Vector Networks" by Vladimir N. Vapnik (1995):

   • While this paper primarily focuses on support vector machines (SVMs), a machine learning technique, SVMs have become an integral part of data mining. They are widely used for classification and regression tasks.

7. "Data Streams: Algorithms and Applications" by S. Muthukrishnan (2005):

   • This paper discusses the challenges and algorithms associated with mining data streams—continuous, high-speed data flows. Handling data streams is crucial in real-time applications and has become an important aspect of data mining.

8. "Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data" by Bing Liu (2007):

   • This paper explores the field of web usage mining, focusing on the discovery and application of usage patterns from web data. It addresses the unique challenges associated with mining user behavior on the web.

These papers represent just a selection of influential works in the field of data mining. The field is dynamic, and new research continues to contribute to its growth and development.